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227 gesichtete, geschützte Fragmente: Plagiat

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I. INTRODUCTION [FN 1]

1.1. OVERVIEW

Terrorists seldom operate in vacuum but interact with one another to carry out terrorist activities. To perform terrorist activities requires collaboration among terrorists. Relationship between individual terrorists for the basis of terrorism and are essential for the smooth operation of a terrorist organization, which can be viewed as network consisting of nodes [FN 2] (for example terrorists, terrorist camps, supporting countries, etc.) and links [FN 3] / ties (relationships). In terrorist networks, there may present some groups/ or cells, within which members have close relationships. One group may also interact with other groups. For example, some key nodes (key players) may act as leaders to control activities of a group. Some others may serve as gatekeepers to ensure smooth flow of information or illicit goods.

[FN 1] Parts of this chapter are already published in Memon N., Hicks David L., and Larsen Henrik Legind. (2007d) Memon N. Lasen H.L. (2006a) (2006b) (2006c);

[FN 2] In this dissertation the words: nodes, actors, players, and vertices are used interchangeably

[FN 3] In this dissertation the words: links, ties, relationships, and edges are used interchangeably

1 Introduction

Criminals seldom operate in a vacuum but interact with one another to carry out various illegal activities. In particular, organized crimes [...] require collaboration among offenders. Relationships between individual offenders form the basis for organized crimes [18] and are essential for smooth operation of a criminal enterprise, which can be viewed as a network consisting of nodes (individual offenders) and links (relationships). In criminal networks, there may exist groups or teams, within which members have close relationships. One group also may interact with other groups [...]. For example, some key members may act as leaders to control activities of a group. Some others may serve as gatekeepers to ensure smooth flow of information or illicit goods.

 Anmerkungen Take somebody else's introduction, shorten it slightly, cross out "criminals" and "offenders" and put "terrorists" instead. That way Nm's introduction was made. (There is no reference to Xu and Chen (2003)) The whole page will be repeated more or less on page 93 of the thesis: Nm/Fragment_093_04 Sichter (Graf Isolan), Bummelchen

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In social network literature, researchers have examined a broad range of types of ties (Katz, N. et al. 2004). These include communication ties (such as who talks to whom or who gives information or advice to whom), formal ties (such as who reports to whom), affective ties (such as who likes whom, or who trust whom), material or work flow ties (such as who gives bomb making material or other resources to whom), proximity ties (who is spatially or electronically close to whom). Networks are typically multiplex, that is, actors share more than one type of tie. For example, two terrorists might have a formal tie (one is foot-[soldier / newly recruited person in terrorist cell and reports to the other, who is the cell leader) and an affective tie (they are friends) and proximity tie (they are residing in the same apartment and their flats are two doors away on the same floor).] [p. 308]

Network researchers have examined a broad range of types of ties. These include communication ties (such as who talks to whom, or who gives information or advice to whom). formal ties (such as who reports to whom), affective ties (such as who likes whom, or who trusts whom). material or work flow ties (such as who gives money or other resources to whom), proximity ties (who is spatially or electronically close to whom), and cognitive ties (such as who knows who knows whom). Networks are typically mutiplex, that is, actors share more than one type of tie. For example, two academic colleagues might have a formal tie (one is an assistant professor and reports to the other. who is the department chairperson)

[p. 309]

and an affective tie (they are friends) and a proximity tie (their offices are two doors away).

 Anmerkungen The exact same section will be used again on pages 93 and 94 of this thesis [1], [2]. Here like there, there is only a passing reference made to the source of this text. Here like there, nothing is marked as a citation. Sichter (Graf Isolan) Agrippina1

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[For example, two terrorists might have a formal tie (one is foot-]soldier / newly recruited person in terrorist cell and reports to the other, who is the cell leader) and an affective tie (they are friends) and proximity tie (they are residing in the same apartment and their flats are two doors away on the same floor).

Network researchers have made a difference between strong ties (such as wife and husband) and weak ties such as colleagues met at a conference (Granovetter, 1973, 1982). This distinction includes affect, mutual obligations, reciprocity, and intensity. Strong ties become valuable when an individual pursues socio-emotional support and should be trustworthy. On the other hand, weak ties become valuable when individuals pursue varied or unique information from external outside their routine contacts.

As per study of the literature, this study found that the ties may be non-directional (Atta attends meeting with Nawaf Alhazmi) or differ in direction (Bin Laden gives advice to Atta vs. Atta gets advice from Bin Laden). They may differ in content (Atta talks with Khalid about the trust of his friends (to be used as human bombs for 9/11) and Khalid about his meeting with Bin Laden), frequency (daily, weekly, monthly, etc.), and medium (frontal conversation, written memos, email, fax, instant messages, live chat, Skype or Facebook messages, etc.). Finally ties may differ in sign, ranging from positive (Iraqis like Zarqawi) to negative (Jordanians dislike Zarqawi).

[p. 308]

For example, two academic colleagues might have a formal tie (one is an assistant professor and reports to the other. who is the department chairperson)

[p.309]

and an affective tie (they are friends) and a proximity tie (their offices are two doors away).

Network researchers have distinguished between strong ties (such as family and friends) and weak ties (such as acquaintances) (Granovetter, 1973. 1982). This distinction can involve a multitude of facets, including affect, mutual obligations, reciprocity, and intensity. Strong ties are particularly valuable when an individual seeks socioemotional support and often entail a high level of trust. Weak ties are more valuable when individuals are seeking diverse or unique information from someone outside their regular frequent contacts. [...]

Ties may be nondirectional (Joe attends a meeting with Jane) or vary in direction (Joe gives advice to Jane vs. Joe gets advice from Jane). They may also vary in content (Joe talks to Jack about the weather and to Jane about sports), frequency (daily. weekly, monthly, etc.), and medium (face-to-face conversation, written memos, e-mail, instant messaging, etc.). Finally, ties may vary in sign, ranging from positive (Joe likes Jane) to negative (Joe dislikes Jane).

 Anmerkungen To be found again here [3]. Sichter (Graf Isolan) Agrippina1

 Bearbeitet: 23. April 2012, 22:47 FieshErstellt: 22. April 2012, 22:58 (Graf Isolan)

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[For example, arrest of central members in a network may affect the operation of a network and put a] terrorist organization out of action (Baker, W. E., Faulkner R. R., 1993; McAndrew, D., 1999; Sparrow, M. K., 1991). Subgroups and interaction patterns between groups are helpful in detecting overall structure of a network (Evan, W. M., 1972; Ronfeldt, D., Arquilla, J., 2001). [For exam]ple, removal of central members in a network may effectively upset the operational network and put a criminal enterprise out of action [3, 17, 21]. Subgroups and interaction patterns between groups are helpful for finding a network’s overall structure, which often reveals points of vulnerability [9, 19].

[EN 3] Baker, W. E., Faulkner R. R.: The social organization of conspiracy: illegal networks in the heavy electrical equipment industry. American Sociological Review, Vol. 58, No. 12. (1993) 837–860.

[EN 17] McAndrew, D.: The structural analysis of criminal networks. In: Canter, D., Alison, L. (eds.): The Social Psychology of Crime: Groups, Teams, and Networks, Offender Profiling Series, III, Aldershot, Dartmouth (1999) 53–94.

[EN 21] Sparrow, M. K.: The application of network analysis to criminal intelligence: An assessment of the prospects. Social Networks, Vol. 13. (1991) 251–274.

[EN 9] Evan, W. M.: An organization-set model of interorganizational relations. In: M. Tuite, R. Chisholm, M. Radnor (eds.): Interorganizational Decision-making. Aldine, Chicago (1972) 181–200.

[EN 19] Ronfeldt, D., Arquilla, J.: What next for networks and netwars? In: Arquilla, J., Ronfeldt, D. (eds.): Networks and Netwars: The Future of Terror, Crime, and Militancy. Rand Press, (2001).

 Anmerkungen Content, references and a number of formulations are identical though the source refers to criminals, Nm to terrorists. This section also appears at Nm/Fragment 095 01. Sichter (Graf Isolan), Bummelchen

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Defeating terrorist networks requires a more nimble intelligence apparatus that operates more actively and makes use of advanced information technology. Data mining for counterterrorism (In the study we call it as investigative data mining [FN 4]) is a powerful tool for [intelligence and law enforcement officials fighting terrorism (DeRosa Mary, 2004).]

[FN 4] The term is firstly used by Jesus Mena in his book Investigative Data Mining and [Criminal Detection, Butterworth (2003).]

Defeating terrorism requires a more nimble intelligence apparatus that operates more actively within the United States and makes use of advanced information technology. Data-mining and automated data-analysis techniques are powerful tools for intelligence and law enforcement officials fighting terrorism.
 Anmerkungen Although the source is given nothing is marked as a citation. Sichter (Graf Isolan), Bummelchen

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Data mining actually has relatively narrow meaning: the approach that uses algorithms to determine analytical patterns in datasets. Subject-based automated data analysis applies models to data to predict behaviour, assess risk, determine associations, or do other type of analysis (DeRosa Mary, 2004). The models used for automated data analysis can be used on patterns discovered by data mining techniques.

Although these techniques are powerful, it is a mistake to view investigative data mining techniques as complete solution to security problems. The strength of investigative data mining (IDM) is to assist analysts and investigators. IDM can automate some tasks that analysts would otherwise have to accomplish manually. It can help to place in order attention and focus an inquiry, and can even do some early analysis and sorting of masses of data. Nevertheless, in the multifaceted world of counterterrorism, it is not likely to be useful as the only source for a conclusion or decision.

“Data mining” actually has a relatively narrow meaning: it is a process that uses algorithms to discover predictive patterns in data sets. “Automated data analysis” applies models to data to predict behavior, assess risk, determine associations, or do other types of analysis. The models used for automated data analysis can be based on patterns (from data mining or discovered by other methods) or subject based, which start with a specific known subject. [...]

Although these techniques are powerful, it is a mistake to view data mining and automated data analysis as complete solutions to security problems. Their strength is as tools to assist analysts and investigators. They can automate some functions that analysts would otherwise have to perform manually, they can help prioritize attention and focus an inquiry, and they can even do some early analysis and sorting of masses of data. But in the complex world of counterterrorism, they are not likely to be useful as the only source for a conclusion or decision.

 Anmerkungen Every now and then a reference to the source is thrown in. Nevertheless nothing of the cited text is marked as such. Sichter (Graf Isolan), Bummelchen

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IDM offers ability to map a covert cell, and to measure the specific structural and interactive criteria of such a cell. This framework aims to connect dots between individuals and to map and measure complex, covert, human groups and organisations (Memon N., and Larsen H. L., 2006c). The method focuses on uncovering the patterning of people’s interaction, and correctly interpreting these networks assists in predicting behaviour and decision-making within the network (Memon N., and Larsen H. L., 2006c). Social network analysis offers the ability to firstly map a covert cell, and to secondly measure the specific structural and interactional criteria of such a cell.

[page 3]

This framework aims to connect the dots between individuals and “map and measure complex, sometimes covert, human groups and organisations”. [EN 8] The method focuses on uncovering the patterning of people’s interaction, [EN 9] and correctly interpreting these networks assists “in predicting behaviour and decision-making within the network”. [EN 10]

[EN 8] Krebs, V. (2002) “Mapping Networks of Terrorist Cells”, Connections, Vol. 24, 3, pp. 43-52.

[EN 9] Freeman, L. (nd) ‘The Study of Social Networks’, The International Network for Social Network Analysis, Retrieved May 17, 2004, from http://www.sfu.ca/~insna/INSNA/na_inf.html.

[EN 10] Renfro, R. & Deckro, R. (2001). “A Social Network Analysis of the Iranian Government”, paper presented at 69th MORS Symposium, 12-14 June, 2001, p. 4.

 Anmerkungen The source is not mentioned anywhere in the thesis. Not only did Nm copy an entire paragraph from the source, he also references for it the paper "Memon N., and Larsen H. L., 2006c", written by himself and the thesis supervisor. Finally he also removes the references for correct quotations in the source. Sichter (Hindemith), Bummelchen

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This technique is also known as subject based link analysis. This technique uses aggregated public records or other large collection of data to find the links between a subject — a suspect, an address, [or a piece of relevant information — and other people, places, or things.] A relatively simple and useful data-analysis tool for counterterrorism is subject-based “link analysis.” This technique uses aggregated public records or other large collections of data to find links between a subject — a suspect, an address, or other piece of relevant information — and other people, places, or things.
 Anmerkungen At the end of the paragraph (on the next page) the source is given. Unfortunately nothing in this paragraph has been marked as a citation. Sichter (Graf Isolan), Bummelchen

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[This technique uses aggregated public records or other large collection of data to find the links between a subject—a suspect, an address,] or a piece of relevant information—and other people, places, or things. This can provide additional clues for analysts and investigators to follow (DeRosa Mary, 2004). This technique uses aggregated public records or other large collections of data to find links between a subject—a suspect, an address, or other piece of relevant information—and other people, places, or things. This can provide additional clues for analysts and investigators to follow.
 Anmerkungen continuation of previous fragment Sichter (Graf Isolan), Bummelchen

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IDM also gives the analyst the ability to measure the level of covertness and efficiency of the cell as a whole, and the level of activity, ability to access others, and the level of control over a network each individual possesses. The measurement of these criteria allows specific counter-terrorism applications to be drawn, and assists in the assessment of the most effective methods of disrupting and neutralising a terrorist cell. In short, IDM provides a useful way of structuring knowledge and framing further research. Ideally it can also enhance an analyst’s predictive capability (Memon N., and Larsen H. L., 2006c). The method also endows the analyst the ability to measure the level of covertness and efficiency of the cell as a whole, and also the level of activity, ability to access others, and the level of control over a network each individual possesses. The measurement of these criteria allows specific counter-terrorism applications to be drawn, and assists in the assessment of the most effective methods of disrupting and neutralising a terrorist cell.

[page 3]

In short, social network analysis “provides a useful way of structuring knowledge and framing further research. Ideally it can also enhance an analyst’s predictive capability”.[EN 12]

[EN 12] Aftergood, S. (2004) ‘Secrecy News: Social Network Analysis and Intelligence’ [online], Federation of American Scientists Project on Government Secrecy, Vol. 2004, 15. Retrieved May 17, 2004, from http://www.fas.org/sgp/ news/secrecy/2004/02/020904.html.

 Anmerkungen The source is not mentioned anywhere in the thesis. Not only did Nm copy an entire paragraph from the source, he also references for it the paper "Memon N., and Larsen H. L., 2006c", written by himself and the thesis supervisor. Finally he also removes the reference to Aftergood (2004) who Koschade correctly quotes for the last sentence. Sichter (Hindemith), Bummelchen

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Traditional data mining normally refers to using techniques rooted in statistics, rule-based logic, or artificial intelligence, machine learning, fuzzy logic, statistics to examine through large amounts of data to find previously unknown but statistically significant patterns. However, the application of IDM in the counterterrorism domain is more challenging, because unlike traditional data mining applications, we must find extremely wide variety of activities and hidden relationships among individuals (Seifert 2006). Table 1.1 gives a series of reasons why traditional data mining is not the same as investigative data mining. Data mining commonly refers to using techniques rooted in statistics, rule-based logic, or artificial intelligence to comb through large amounts of data to discover previously unknown but statistically significant patterns. However, the general counterterrorism problem is much harder because unlike commercial data mining applications, we must find extremely rare instances of patterns across an extremely wide variety of activities and hidden relationships among individuals. Table 2 gives a series of reasons for why commercial data mining isn’t the same as terrorism detection in this context.
 Anmerkungen no source given Sichter (Graf Isolan) Agrippina1

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Although SNA is not conventionally considered as data mining technique, it is especially suitable for mining a large volume of association data to discover hidden structural patterns in terrorist networks. Although SNA is not traditionally considered as a data mining technique, it is especially suitable for mining large volumes of association data to discover hidden structural patterns in criminal networks [9, 10].
 Anmerkungen Right before he starts to massively present material form Koelle et al. (2006), for which he gives no reference, he puts in a small section, adapted in the usual way, from Xu and Chen (2005a), which he also does not mark as a citation and for which he also does not give a reference. Pure patchwork. Sichter (Graf Isolan), Bummelchen

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Social network analysis (SNA) primarily focuses on applying analytic techniques to the relationships between individuals and groups, and investigating how those relationships can be used to infer additional information about the individuals and groups (Degenne and Forse, 1999). There are a number of mathematical and algorithmic approaches that can be used in SNA to infer such information, including connectedness and centrality (Wasserman and Faust, 1994).

SNA is used in a variety of domains. For example, business consultants use SNA to identify the effective relationships between workers that enable work to get done; these relationships often differ from connections seen in an organizational chart (Ehrlich and Carboni, 2005). Law enforcement personnel have used social networks to analyze terrorist networks (Krebs, 2002; Stewart, 2001) and criminal networks (Sparrow, M. K., 1991). The capture of Saddam Hussein was facilitated by social network analysis: military officials constructed a network containing Hussein’s tribal and family links, allowing them to focus on individuals who had close ties to Hussein (Hougham, 2005).

Social network analysis (SNA) primarily focuses on applying analytic techniques to the relationships between individuals and groups, and investigating how those relationships can be used to infer additional information about the individuals and groups (Degenne & Forse, 1999). There are a number of mathematical and algorithmic approaches that can be used in SNA to infer such information, including connectedness and centrality (Wasserman & Faust, 1994).

SNA is used in a variety of domains. For example, business consultants use SNA to identify the effective relationships between workers that enable work to get done; these relationships often differ from connections seen in an organizational chart (Ehrlich & Carboni, 2005). Law enforcement personnel have used social networks to analyze terrorist networks (Krebs, 2006; Stewart, 2001) and criminal networks (Sparrow, 1991). The capture of Saddam Hussein was facilitated by social network analysis: military officials constructed a network containing Hussein’s tribal and family links, allowing them to focus on individuals who had close ties to Hussein (Hougham, 2005).

 Anmerkungen Identical, with the source not even being mentioned in Nm's thesis. Sichter (Graf Isolan), Hindemith

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There is a pressing need to automatically collect data of terrorist networks, analyze such networks to find hidden relations and groups, prune datasets to locate regions of interest, detect key players, characterize the structure, trace points of vulnerability, and find efficiency (how fast communication is being made) of the network. There is a pressing need to automatically collect data on social systems as rich network data, analyze such systems to find hidden relations and groups, prune the datasets to locate regions of interest, locate key actors, characterize the structure, locate points of vulnerability, and simulate change in a system as it evolves naturally or in response to strategic interventions over time or under certain impacts, including modifcation of data.
 Anmerkungen Only the last half-sentence is substantially different to the source, which is not mentioned anywhere in the thesis. Sichter (Hindemith), Bummelchen

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Taking an example of 9/11 attack, it is possible to show, that simple IDM techniques discussed above can be used to assist terrorist investigation. IDM techniques using government watch list information, airline reservation records, and aggregated public [record data, could have identified all 19 hijackers of 9/11 terrorists attacks before the attack. (DeRosa Mary, 2004):] A hindsight analysis of the September 11 attacks provides an example of how simple, subject-based link analysis could be used effectively to assist investigations or analysis of terrorist plans. By using government watch list information, airline reservation records, and aggregated public record data, link analysis could have identified all 19 September 11 terrorists — for follow-up investigation — before September 11. [FN 16]

[FN 16] Of course, this kind of analysis will always appear neater and easier with hindsight, but it is a useful demonstration nonetheless.

 Anmerkungen Only a cursory reference to the source is made, and it is not clear if it refers to what has come before or to what will come; nothing is marked as a citation. Sichter (Graf Isolan), Bummelchen

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[IDM techniques using government watch list information, airline reservation records, and aggregated public] record data, could have identified all 19 hijackers of 9/11 terrorists attacks before the attack. (DeRosa Mary, 2004):

&bullet; Watch List Information (Direct Links)

o Khalid Almindhar and Nawaf Alhazmi, both were involved in 9/11 hijacking and were on U.S. government terrorist watch list.

o Ahmed Alghamdi, who hijacked United Airlines (UA) Flight 175, and crashed it into the World Trade Center South Tower, was on an Immigration and Naturalization Service (INS) watch list for illegal or expired visas.

It is noteworthy to specify all three of the above given terrorists used their real names to reserve the flights.

&bullet; Link Analysis (One Degree of Separation)

o Muhammad Atta and Mavan Al shehhi, both hijackers used same contact address for their flight reservations that Khalid Almihdhar used for his flight reservation.

o Salem Alhazmi, used the same contact address on his reservation as Nawaf Alhazmi.

o Majed Moqed used the same frequent flyer number that Khalid Al mindhar used in his reservation.

o Hamza Alghamdi, used the same contact address on his reservation as Ahmed Alghamdi used on his reservation.

o Hani Hanjour, lived with both Nawaf Alhazmi and Khalid Almihdhar, a fact that searches of public records could have revealed.

[p. 6]

By using government watch list information, airline reservation records, and aggregated public record data, link analysis could have identified all 19 September 11 terrorists—for follow-up investigation—before September 11. [FN 16] The links can be summarized as follows:

[p. 7]

Direct Links — Watch List Information

Khalid Almihdhar and Nawaf Alhazmi, both hijackers of American Airlines (AA) Flight 77, which crashed into the Pentagon, appeared on a U.S. government terrorist watch list. Both used their real names to reserve their flights.

Ahmed Alghamdi, who hijacked United Airlines (UA) Flight 175, which crashed into the World Trade Center South Tower, was on an Immigration and Naturalization Service (INS) watch list for illegal or expired visas. He used his real name to reserve his flight.

Link Analysis — One Degree of Separation

Two other hijackers used the same contact address for their flight reservations that Khalid Almihdhar listed on his reservation. These were Mohamed Atta, who hijacked AA Flight 11, which crashed into the World Trade Center North Tower, and Marwan Al Shehhi, who hijacked UA Flight 175.

Salem Alhazmi, who hijacked AA Flight 77, used the same contact address on his reservation as Nawaf Alhazmi.

The frequent flyer number that Khalid Almihdhar used to make his reservation was also used by hijacker Majed Moqed to make his reservation on AA Flight 77.

Hamza Alghamdi, who hijacked UA Flight 175, used the same contact address on his reservation as Ahmed Alghamdi used on his.

Hani Hanjour, who hijacked AA Flight 77, lived with both Nawaf Alhazmi and Khalid Almihdhar, a fact that searches of public records could have revealed.

 Anmerkungen continued from previous page; most formulations are taken from the original, nothing is marked as a citation. Sichter (Graf Isolan), Bummelchen

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“How can we mine terrorist networks?” Traditional methods of machine learning and data mining, taking, as input, a random sample of homogeneous objects from a single relation, may not be appropriate here. The data comprising terrorist networks tend to be heterogeneous, multi-relational, and semi-structured. IDM embodies descriptive and predictive modeling. By considering links (the relationship between the objects), the more information is made available to the mining process. This brings about several new tasks.

(1) Group detection. Group detection is a clustering task. It predicts when sets of objects belong to the same group or cluster, based on their attributes as well as their link [structure.]

“How can we mine social networks?” Traditional methods of machine learning and data mining, taking, as input, a random sample of homogenous objects from a single

[page 561]

relation, may not be appropriate here. The data comprising social networks tend to be heterogeneous, multirelational, and semi-structured.

[...]

[page 562]

[...]

7. Group detection. Group detection is a clustering task. It predicts when a set of objects belong to the same group or cluster, based on their attributes as well as their link structure.

 Anmerkungen Taken from a textbook on data-mining without reference. Sichter (Hindemith), WiseWoman

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(2) Sub-graph detection. Subgraph identification finds characteristic subgraphs within networks. This is a form of graph search and also known as graph filtering technique.

(3) Object classification. In traditional classification methods, objects are classified on the attributes that describe them. Link-based classification predicts the category of an object-based not only on attributes, but also on links, and on the attributes of the linked objects.

[page 562]

8. Subgraph detection. Subgraph identification finds characteristic subgraphs within networks. This is a form of graph search and was described in Section 9.1.

[page 561]

1. Link-based object classification. In traditional classification methods, objects are classified based on the attributes that describe them. Link-based classification predicts the category of an object based not only on its attributes, but also on its links, and on the attributes of linked objects.

 Anmerkungen Taken from a textbook on data-mining without reference Sichter (Hindemith), Graf Isolan

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1.5. LIMITATIONS

While traditional SNA has been used to successfully derive insights into a social network, it can be restrictive for a number of reasons. SNA assumes a well-formed social network, but real-world methods of data collection may not ensure that the resulting social network is complete and contains needed data. SNA focuses primarily on the existence of a relationship between nodes in the network, but not on attributes of that relationship or the nodes in the relationship. Furthermore, SNA does not explicitly consider the uncertainty of attributes on nodes or relationships. Finally, graph-theoretic algorithms used in SNA tend to focus on homogenous set of entities and relationships, making it difficult to analyze networks that involve a heterogeneous set of nodes connected by a variety of link types.

1.5.1 Issues in Data Collection [FN 7]

[FN 7] The text in this subsection is partially published in Memon Nasrullah and Larsen Henrik Legind. (2007e)

2. LIMITATIONS OF SOCIAL NETWORK ANALYSIS

While traditional SNA has been used to successfully derive insights into a social network, it can be restrictive for a number of reasons. SNA assumes a well-formed social network, but real-world methods of data collection may not ensure that the resulting social network is complete and contains needed data. SNA focuses primarily on the existence of a relationship between nodes in the network, but not on attributes of that relationship or the nodes in the relationship. Furthermore, SNA does not explicitly consider the uncertainty of attributes on nodes or relationships. Finally, graph-theoretic algorithms used in SNA tend to focus on a homogenous set of entities and relationships, making it difficult to analyze networks that involve a heterogeneous set of nodes connected by a variety of link types.

2.1 ISSUES IN DATA COLLECTION

 Anmerkungen Identical, with the source not being mentioned anywhere in Nm's thesis. Sichter (Graf Isolan), Hindemith

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1.5.1 Issues in Data Collection[FN 7]

Data collection is difficult for any network analysis because it is hard to create a complete network. It is especially difficult to gain information on terrorist networks. Terrorist organizations do not provide information on their members, and the government rarely allows researchers to use their data. A number of academic researchers focus primarily on data collection on terrorist organizations, analyzing the information through description and straightforward modeling. Valdis Krebs was one of the first to collect data using public sources with his 2001 article in Connections. In this work, Krebs creates a pictorial representation of the al Qaeda network (as shown in Figure 1.4) responsible for 9/11 that shows the many connections between the hijackers of the four airplanes. Similarly, After the Madrid bombing in 2004, Spanish sociologist Jose A. Rodriguez conducted an analysis to map the March 11th terrorist network.

[FN 7] The text in this subsection is partially published in Memon Nasrullah and Larsen Henrik Legind. (2007e)

Data Collectors

Data collection is difficult for any network analysis because it is hard to create a complete network. It is especially difficult to gain information on terrorist networks. Terrorist organizations do not provide information on their members, and the government rarely allows researchers to use their intelligence data. A number of academic researchers focus primarily on data collection on terrorist organizations, analyzing the information through description and straightforward modeling. Valdis Krebs was one of the first to collect data using public sources with his 2001 article in Connections. In this work, Krebs creates a pictorial representation of the al Qaeda network responsible for 9/11 that shows the many ties between the hijackers of the four airplanes. After the Madrid bombing in 2004, Spanish sociologist Jose A. Rodriguez completed an analysis similar to Krebs’ by using public sources to map the March 11th terrorist network.

 Anmerkungen The conference, the proceedings of which "this subsection is partially published" in, was held on 4-6 July 2007. Thus the unnamed source predates (again) the text by Nm. It should also be noticed that the coauthors of the conference paper by Nm are his thesis advisor and the chair of his thesis committee. Note: in the bibliography there are two entries (2007e): "Memon Nasrullah and Larsen Henrik Legind. (2007e)" as well as "Memon Nasrullah, Hicks David L., and Larsen Henrik Legind (2007e)". Only the latter has common text fragments with the chapter 1.5.1 of the dissertation and therefore it was assumed that in FN 7 this publication was meant. Sichter (Graf Isolan), Hindemith

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Understanding Terror Networks by Marc Sageman was another brilliant attempt in this regard. He using public sources, collected biographies of 172 Islamic terrorist operatives affiliated with the global Salafi jihad (the violent revivalist Islamic movement led by al Qaeda). He also used social network analysis specifically on Al Qaeda operatives since 1998.

However collecting terrorists’ information from open sources has a few key drawbacks. With open sources, if the author does not have information on terrorists, he or she assumes they do not exist.

Another bright spot is the 2004 publication of Understanding Terror Networks by Marc Sageman. Using public sources, Sageman collects biographies of 172 Islamic terrorist operatives affiliated with the global Salafi jihad (the violent revivalist Islamic movement led by al Qaeda). He uses social network analysis specifically on Al Qaeda operatives since 1998. [...]

Despite their many strengths, Krebs’ and Sageman’s works have a few key drawbacks. By dealing with open sources, these authors are limited in acquiring data. With open sources, if the author does not have information on terrorists, he or she assumes they do not exist.

 Anmerkungen Continued from previous page. Sichter (Graf Isolan), Hindemith

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If one cannot find an al Qaeda operative in publicly available sources in Denmark, the researcher could assume there is no al Qaeda network in Denmark. If one cannot find an al Qaeda operative in the U.S. in publicly available sources, the researcher could assume there is no al Qaeda network.
 Anmerkungen This sentence is the almost seamless continuation of the section which Nm began to copy on pages 33 and 34 of his thesis. Sichter (Graf Isolan), Agrippina1, Hindemith

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Social network analysis does not fully report the need to characterize different types of links. A link in a social network graph can represent a variety of concepts and relationships. For example: an evaluation (A likes B or A respects B), behavioural interaction, similarity, association, affiliation, physical connection, formal relations, (such as authority), and biological relations (Wasserman & Faust, 1994).

Links can be of different types. Link types can also indicate the strength of a particular concept. For example, link type “friendship” can be further classified four subtypes: “No contact”, “small talk and coffee”, “exchange of favours”, and “close ties” (Heran, F., 1987).

While traditional graph based algorithms used for SNA may incorporate analysis of different node and link types. However, they incline to be homogeneous within a network (i.e., considering a single node or link type per analysis), rather than being heterogeneous within a network (i.e., multiple link and node types). Further, graph based algorithms do not typically consider attributes on links or nodes.

2.2 HOMOGENEOUS NODE AND LINK TYPES

Social network analysis does not fully address the need to characterize different types of relationships. A social network graph can represent a variety of concepts through links, such as evaluation (A likes B, A respects B), behavioral interaction, transfers of material resources, association, affiliation, movement between places or statuses, physical connection, formal relations (such as authority), and biological relations (Wasserman & Faust, 1994).

Different links types can also indicate the strength of a particular concept. For example, one social network construction study offered subjects four choices to identify the intensity of their friendships: “No contact”, “small talk and coffee”, “exchange of favors”, and “close ties” (Heran, 1987).

While traditional graph-theoretic algorithms used for SNA may incorporate analysis of different node and link types, they tend to be homogeneous within a network (i.e., considering a single node or link type per analysis), rather than being heterogeneous within a network (i.e., multiple link and node types). Further, graph-theortic algorithms do not typically consider attributes on links or nodes [...]

 Anmerkungen Highly similar, with the source not being mentioned anywhere in Nm's thesis. Sichter (Graf Isolan), Hindemith

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One of the variations is link analysis. It is the process of building up networks of interconnected objects through relationships in order to expose patterns and trends. Link analysis uses item-to-item associations to generate networks of interactions [and connections from defined datasets.] Link analysis is the process of building up networks of interconnected objects through relationships in order to expose patterns and trends. Link analysis uses item-to item associations to generate networks of interactions and connections from defined data sets.
 Anmerkungen Keine Kennzeichnung als Zitat, kein Hinweis auf die Quelle Sichter (Graf Isolan) WiseWoman

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[Link analysis uses item-to-item associations to generate networks of interactions] and connections from defined datasets. The diagrams of link analysis have a variety of names ranging from entity-relationship diagrams and connected networks to nodes-and-links and directed graphs. The methods of link analysis add dimensions to an analysis that the other form of visualization do not support.

By explicitly representing relationships among objects, an entirely different perspective on how the data can be analysed and the types of patterns that can be discovered. Link analysis systems were initially used primarily in investigative world (e.g., law enforcement), but they have recently made significant inroads into a variety of commercial applications. One potential drawback of link analysis is that the aggregate number of data records that can be presented in most diagrams is somewhat limited, as compared to other visualization paradigms. As a result, the analyses tend to focus on verifying subsets of large datasets. Nevertheless, link analysis provides a powerful means of performing visual data mining, particularly if we know how to take advantage of layout options, filter assessments, and presentation formats. The link analysis systems allow the identification of patterns, emerging groups, and generational connections quickly.

 Anmerkungen Only the slightest of changes, it is not marked as a citation, and no source is given Sichter (Graf Isolan) WiseWoman

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In addition to the previous discussion, link analysis is basically a descriptive approach for exploring data in order to identify relationship among values in the database. In this approach, the user lays out graphically; the links between various entities; such as people, resources, and locations. A key limitation of this approach is that it is a primary a tool for visualization and organization. There are no analytic metrics attached. As such, there are no

procedures for analyzing the data estimating who or what should be targeted to achieve what effects, or for estimating [...]

Visual link analysis is basically a descriptive approach to exploring data in order to identify relationships among values in a database. In this approach, the user lays out graphically the links between various entities such as people, resources, and locations. A key limitation of this approach is that it is primarily a tool for visualization and organization. There are no analytic metrics or attached. As such, there are no procedures for analyzing the data and estimating what data is missing, who or what should be targeted to achieve what effects, or for estimating [...]
 Anmerkungen nothing is marked as a citation, source is not given. Sichter (Graf Isolan), Hindemith

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After 9/11 attacks, the academic world has increased the attention paid to the analysis of terrorist networks because of public interest. Network analysis of terrorist organizations is divided into two groups: data collectors and data modelers:

Data collection is difficult for network analysis because it is hard to create a complete network (as already discussed). It is especially difficult to gain information on terrorist networks. Terrorist organizations do not provide information on the members, and governments rarely allow researchers to use their intelligence data.

There has been limited work in the field of complex modeling of terrorist networks.

[p. 4]

Since the winter of 2001, the academic world has increased the attention paid to the social network analysis of terrorism as a result of public interest and new grant money. [FN 15] Network analysis of terrorist organizations continues to grow and can be divided into two groups: the data collectors and the modelers.

[...]

Data collection is difficult for any network analysis because it is hard to create a complete network. It is especially difficult to gain information on terrorist networks. Terrorist organizations do not provide information on their members, and the government rarely allows researchers to use their intelligence data.

[p. 5]

There has been limited work in the field of complex modeling of terrorist networks outside the work of Kathleen Carley and her associates.

 Anmerkungen The paragraph from line 26 to 30 can already be found on page 33 Nm/Fragment_033_15 of Nm's thesis. Comparing these two paragraphs, one can see directly how Nm tries to camouflage his copying Sichter (Graf Isolan), Hindemith

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[A common problem for the modelers is the] issue of data. Any academic work is only as good as the data, no matter the type of advanced methods used. It is, well known fact that modelers often do not have the best data and they do not have access to classified data. Many of the models are created without data or with incomplete data. The implication of this is that the results can be potentially misleading. A common problem for the modelers is the issue of data. Any academic work is only as good as the data, no matter the type of advanced methods used. Modelers often do not have the best data, as they have not collected individual biographies (like Sageman) and do not have access to classified data. Many of the models are created data-free or without complete data, yet do not fully consider human and data limitations. The implication of this is that the results can be potentially misleading, [...]
 Anmerkungen Continued from last page: no reference to the source is given. Sichter (Graf Isolan), Hindemith

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The increase of number of terrorism incidents in recent years has increased the feeling of insecurity and danger in many countries. In this regard, classified information with government agencies is not available to the public or academic research institutions. However, the Internet revolution has opened up a new ways by providing huge volume of information on these incidents. Such information in open domain can be a used to create a knowledge base which, after proper cleaning could be useful for intelligence analysis. The increase in violent terrorist incidents, in recent years, has heightened the feeling of insecurity in many countries. In this regard, privileged information with government agencies is not available to the public or academic research institutes. However, the IT revolution has opened up a growing volume of information on these incidents. This information in the open domain can be a valuable resource in creating a data base which, after proper capture, cleaning and classification could be useful in strategic analysis
 Anmerkungen The source is not mentioned in the entire thesis. Sichter (Hindemith), Bummelchen

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This website is database of open source information about the Al Qaeda terrorist network, developed as a research project of the FMS Advanced Systems Group. The goal of creating this database is to apply new technologies and software engineering [approaches to open source intelligence while providing researchers and analysts with information about Al Qaeda.] TrackingTheThreat.com is database of open source information about the Al Qaeda terrorist network, developed as a research project of the FMS Advanced Systems Group. The goal is to apply new technologies and software engineering approaches to open source intelligence while providing researchers and analysts with information about Al Qaeda.
 Anmerkungen the source is not given Sichter (Hindemith), Graf Isolan

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[The goal of creating this database is to apply new technologies and software engineering] approaches to open source intelligence while providing researchers and analysts with information about Al Qaeda. The goal is to apply new technologies and software engineering approaches to open source intelligence while providing researchers and analysts with information about Al Qaeda.
 Anmerkungen Source is not given. Copied text starts on the previous page. Sichter (Hindemith), Graf Isolan

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In order to create a huge database, FMS Advanced Systems Group collected real-world data—information about the people, places, events, connections, and other metadata about Al Qaeda in general, and 9/11 in particular. Several months of research using opensource materials, such as articles on the web, books, magazines, and other information yielded Al Qaeda dataset—a large collection of structured entity and relationship information. In order to test the prototype system, we needed real-world data—information about the people, places, events, connections, and other metadata about Al Qaeda in general, and 9/11 in particular. Several months of research using open-source materials, such as articles on the web, books, magazines, and other information yielded our initial Al Qaeda dataset—a large collection of structured entity and relationship information.
 Anmerkungen not marked as a citation, no source given Sichter (Graf Isolan), Bummelchen

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Terrorist and criminal organizations, generally organize themselves as secret networks with distributed work share. It is difficult to obtain information in the open domain on their membership, links and management or confirm the validity of such information. Still then, due to the need of publicity for the terrorists, for their cause, the open source information becomes relevant in exploring terrorist networks. Terrorist and criminal organisations, generally, organise themselves as secret networks with distributed work share. It is not easy to obtain confirmed information in the open domain on their membership, ties and management. However, terrorists need the oxygen of publicity for their ‘cause’; and, hence, open source information [EN 1] becomes relevant in exploring such networks.
 Anmerkungen No source is given for this short paragraph. Sichter (Hindemith), Bummelchen

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It would be quite difficult to model the network structure and evolution of Al Qaeda since many of the organizations that claim ties to Al Qaeda are lying and do not actually have those ties. It can be quite difficult differentiating these groups from other, truly loosely affiliated groups. For example, it would be quite difficult to model the network structure and evolution of al Qaeda since many of the organizations that claim ties to al Qaeda are lying and do not actually have those ties. It can be quite difficult differentiating these groups from other, truly loosely affiliated groups.
 Anmerkungen One more paragraph that Nm has taken from Ressler (2006) leaving it unchanged, not marking it as a citation and without referencing the source. Sichter (Graf Isolan), Hindemith

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1.8. PROBLEM DEFINITION

The primary objective of this research is to expand data mining research, sociological, and behavioural theory relevant to the study of terrorist networks, thereby providing theoretical foundations for new and useful methodologies to analyze terrorist networks. For the purposes of this research, terrorist networks are those trying to hide their structures or are unwilling to provide evidence regarding their actions (Sparrow, 1991; Van Meter, 2002).

1.2 Problem Definition

The overarching objective of this research is to expand operations research, sociological, and behavioral theory relevant to the study of social networks, thereby providing theoretical foundations for new and useful methodologies to analyze noncooperative organizations. [...] For the purposes of this research, non-cooperative organizations are those trying to hide their structures or are unwilling to provide information regarding their operations; [...] [cf., Sparrow, 1991; van Meter, 2002].

 Anmerkungen The original source Hamill (2006) is not given. The publication Van Meter (2002) is not listed in the bibliography. Sichter (Hindemith) Agrippina1

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Terrorist networks are so often challenging to reason about and manage. These networks differ on many dimensions. For examples, terrorist networks range in complexity and lethalness of the weaponries they use, the level and source of their fiscal support, their core organizational structure, and their linking to organized crime, the local police and other terrorist or rebellious groups. Despite these differences, in general, these groups rely on communication groups, with and without advanced information technology, to employ, organize, design, direct, and perform terrorist attacks. As such targeting mechanisms aimed at information, channels, and actors can be used to classify those relations and nodes that are actual targets for disrupting the organizational movement or interrelation of these terrorist networks. Investigative data mining provides a means for detecting such targets and assessing the impact of different courses of action on the terrorist networks. [page 51]

Covert networks are often difficult to reason about and manage. [...] These groups[FN 1] vary on many dimensions. For example, terrorist groups range in the sophistication and deadliness of the weapons they use, the level and source of their financial support, their internal organizational structure, and their connection to organized crime, the local police and other terrorist or insurgent groups. Despite these differences, in general, these groups rely on communication networks, with and without advanced information technology, to recruit, organize, plan, direct, and

[page 52]

execute terrorist acts. As such, targeting mechanisms aimed at information, channels, and actors can be used to identify those links and nodes that are effective targets for destabilizing the organizational activity or cohesion of these covert networks. Dynamic network analysis provides a means for identifying such targets and assessing the impact of alternate courses of action on these covert networks.

 Anmerkungen Only small bits and pieces have been exchanged; otherwise the text has been left more or less intact. No reference to the source is given. Sichter (Graf Isolan), Hindemith

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IDM offers the ability to map a covert cell, and to measure the specific structural criteria of such a cell. This framework aims to connect the dots between individuals and “map and measure complex, covert, human groups and organisations”. The method focuses on uncovering the patterning of people’s interaction, and correctly interpreting these networks assists “in predicting behaviour and decision-making within the network”. [p. 2]

Social network analysis offers the ability to firstly map a covert cell, and to secondly measure the specific structural and interactional criteria of such a cell.

[p. 3]

This framework aims to connect the dots between individuals and “map and measure complex, sometimes covert, human groups and organisations”. [EN 8] The method focuses on uncovering the patterning of people’s interaction, [EN 9] and correctly interpreting these networks assists “in predicting behaviour and decision-making within the network”. [EN 10]

---

[EN 8] Krebs, V. (2002) “Mapping Networks of Terrorist Cells”, Connections, Vol. 24, 3, pp. 43-52.

[EN 9] Freeman, L. (nd) ‘The Study of Social Networks’, The International Network for Social Network Analysis, Retrieved May 17, 2004, from http://www.sfu.ca/~insna/INSNA/na_inf.html.

[EN 10] Renfro, R. & Deckro, R. (2001). “A Social Network Analysis of the Iranian Government”, paper presented at 69th MORS Symposium, 12-14 June, 2001, p. 4.

 Anmerkungen While the original text gives references for each sentence, Nm has only left the quotation marks and gives neither the original sources nor the source of this section. Furthermore Nm uses this text in his thesis for the second time, the other is Nm/Fragment_025_22 Sichter (Graf Isolan), Bummelchen

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1.9. RESEARCH OBJECTIVES

The primary objective of this research is to develop the underlying theory and allied methodology used to produce and analyze terrorist networks and proposes mathematical methods for destabilizing these adversaries. The specific objectives of this research include to:

• Develop a new centrality-like measure, via extensions of several others in use to screen networks for potential actors of interest. The theoretical bases that make this measure more amenable to terrorist networks, advantages over other measures are presented.
1.4 Research Objectives

The primary objective of this research is to develop the underlying theory and associated methodology used to generate and analyze courses of action that may be applied to networks of non-cooperative individuals. [...]

Specific objectives of this research include:

1. Develop a new centrality-like measure, via extensions of several others in use, to screen networks for potential actors of interest. The theoretical bases that [page 10] make this measure more amenable to non-cooperative networks, advantages, and computational challenges are presented.

 Anmerkungen no source given: even the research objectives are partially copied. Sichter (Hindemith) Agrippina1

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&bullet; Combine the most promising techniques into a prototype tool-set, developed in Java, for intelligence analysis.

1.10. DISSERTATION OVERVIEW

The organization of this dissertation document is as follows. Chapter 2 presents the background literature relevant to the problem areas and builds the case for the contribution objectives described above. Chapter 3 [...]

7. Combine the most promising techniques into a prototype tool-set, developed in MATLAB, for intelligence analysis use by the sponsoring organizations.

1.5 Dissertation Overview

The organization of this dissertation document is as follows. Chapter II presents the literature relevant to the problem areas and builds the case for the contribution objectives described above. Chapter III [...]

 Anmerkungen no source given, nothing marked as a citation Sichter (Graf Isolan), bummelchen

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Chapter 5 explores the nuances of persuasion and power theory in order to estimate hidden hierarchy from nonhierarchical / horizontal terrorist networks. Some case studies are provided for illustrative purposes throughout the document. Chapter VII explores the nuances of persuasion and power theory in order to estimate gains and losses of information or influence as a function of sender-receiver interactions. Although smaller examples are provided for illustrative purposes throughout the document, [...]
 Anmerkungen continues the using of bits and pieces from Hamill's "Dissertation Overview"; no reference given Sichter (Graf Isolan), Bummelchen

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Chapter 9 provides an overall, general conclusions as well as recommendations for future research. Chapter IX provides overall, general conclusions as well as recommendations for future research.
 Anmerkungen Finishes the copying of bits and pieces from Hamill's (2006) Section 1.5. Sichter (Graf Isolan), Bummelchen

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2.1 OVERVIEW[FN 9]

“Terrorism is a psychological act that communicates through the medium of violence or the threat of violence” (Gunaratna, R., 2000). Terrorist strategies will be aimed at publicly causing damage to symbols or inspiring horror. Timing, location, and method of attacks accommodate media dissemination and ensure “newsworthiness” to maximize impact.

A terrorist operation will often have the goal of manipulating popular perceptions, and will achieve this by controlling or dictating media coverage. This control need not be evident, as terrorists analyze and exploit the dynamics of major media outlets and the pressure of the “news cycle” (Hoffman, B., 1998). The bombing of commuter trains in Madrid is one example of such theory. The true cause behind Madrid bombing is not determined yet. However, one view is that terrorists who specifically planned to influence the political process in Spain conducted the attacks. They believed that the public would feel the current government responsible, as the large percentage of population was against the involvement of Spanish forces in Iraq war. The attacks occurred during the morning rush hour just three days prior to the national elections. Timing the attack played a vital role in maximizing casualties on the trains (killing 191 people and injuring more than 1800), and immediate world over news coverage. Although, there are fair chances of coincidence, but an anti-war Socialist prime minister was elected in the following election who quickly withdrew Spain’s military forces from Iraq.

[FN 9] Some parts of this Sections are taken from ”A Military Guide to Terrorism in 21st Century“

[FN 110]

[...]

Terrorism is a psychological act that communicates through the medium of violence or the threat of violence. Terrorist strategies will be aimed at publicly causing damage to symbols or inspiring fear. Timing, location, and method of attacks accommodate media dissemination and ensure “newsworthiness” to maximize impact.

A terrorist operation will often have the goal of manipulating popular perceptions, and will achieve this by controlling or dictating media coverage. This control need not be overt, as terrorists analyze and exploit the dynamics of major media outlets and the pressure of the “news cycle.”[FN 111] A terrorist attack that appears to follow this concept was the bombing of commuter trains in Madrid, Spain in March 2004. [...] One view is that Islamic terrorists who specifically planned to influence the political process in Spain conducted the attacks. They believed that the large percentage of the Spanish population opposed the war in Iraq and would feel that the current government was responsible for the bombings, and would therefore vote for the opposition. The attacks occurred during morning rush hour just three days prior to national elections. The timing facilitated maximum casualties on the trains (killing 191 people and injuring more than 1800), plus immediate news coverage throughout the world of the carnage resulting from this terrorist attack. Although it cannot definitively be linked to the bombings, an anti-war Socialist prime minister was elected who quickly withdrew Spain’s military forces from Iraq.

[FN 110] Rohan Gunaratna, “Suicide Terrorism: a Global Threat,” Jane’s Intelligence Review (20 October 2000): 1-7; available from http://www.janes.com/security /international_security/ news/usscole/jir001020_1_n.shtml; Internet; accessed 7 September 2002.

[FN 111] Bruce Hoffman, Inside Terrorism (New York: Columbia University Press, 1998), 136-142.

 Anmerkungen The footnote mentions the source (however without telling the version of the document, nor any bibliographical details). This is done in a way that makes not clear to the reader, what exactly is taken from the source. Sichter (Graf Isolan), Hindemith

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[A] massively launched attack against a target which will not yield enough media coverage is not viable for terrorists as compared to a small attack against a “media accessible” target. However, with the spread of the global media, many locations have potential to become attractive targets that would not have been considered thirty or forty years ago.

The 1998 bombings of the American embassies in Kenya and Tanzania are an example showing how these two relatively unimportant posts created a global sensation because of the modern media coverage. Forty years ago it would have taken days for the international news media to get photographs and relevant text from these locations, making them much less attractive targets in those days. However, with modern technology, it was possible to provide immediate broadcast coverage of the incident. Since the religious justification was the known cause behind the attacks, but still the worldwide coverage of these attacks made it possible for these terrorists to pose as champions of a cause, even in the absence of any effective work at the grassroots level of society (Kepel, G., 2002). The September 11, 2001 bombing of the World Trade Center in New York City was observed live on television by millions of people

[p 2-10] In considering possible terrorist targets, recognize that a massively destructive attack launched against a target that cannot or will not attract sufficient media coverage to impact the target audience is not a viable target for terrorists. A small attack against a “media accessible” target is better than a larger one of less publicity. However, the spread of the global media makes many locations attractive targets that would not have been remotely considered thirty or forty years ago. The 1998 bombings of the American embassies in Kenya and Tanzania illustrate how these two relatively unimportant posts created a global sensation because of the media coverage. Forty years ago it would have taken days for the international news media to get still photographs and some text from these locations, making them much less attractive targets. However, with today’s modern technology, media reporters were able to provide immediate broadcast coverage of the bombings. Since the Islamist factions that conducted the attacks used religious justifications for their actions, the worldwide coverage of these attacks made it possible for these terrorists to pose as champions of a cause, even in the absence of any effective work at the grassroots level of society. [FN 112] The September 11, 2001 bombing of

[FN 112] Gilles Kepel, Jihad: The Trail of Political Islam (Cambridge: The Belknap Press of Harvard University Press): 320.

[p. 2-11]

the World Trade Center in New York City was observed by millions of people worldwide on live television as the successive attacks occurred and sensational mass destruction followed.

 Anmerkungen continued from the previous page. Note FN 9 from the previous page: "Some parts of this Sections are taken from 'A Military Guide to Terrorism in 21st Century'" Sichter (Graf Isolan), Hindemith

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2.2 TYPES OF TERRORISTS

Bedi Rohan (2005) investigated four types of terrorists:

1) Unknown persons: The person who is inspired by a cause and wants to become a terrorist, but due to absence of an experienced counsellor is likely to fall in this type of terrorists. Such persons often get caught early because of the sheer incompetence of their schemes. These can also behave like lone operators in specific situations.

2) New terrorists: The people who are indoctrinated at some [religious school preaching extremism, who commit an act of terrorism just or shortly after being brainwashed and trained.]

Four Types of Terrorists

1) Unknown persons (who could also be lone operators) who are inspired by a cause and want to become terrorists but can’t find a more experienced mentor. Such persons often get caught early because of the sheer incompetence of their schemes.

2) New terrorists indoctrinated at some religious school preaching extremism who commit an act shortly after being brainwashed and trained.

 Anmerkungen Even though the reference is given, the reader is left in the dark about how close to the original text Nm stays. Literally copied text is not marked as such. Also refer to the next page Nm/Fragment_051_01, where more text is copied word for word. Sichter (Graf Isolan), Hindemith

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[2) New terrorists: The people who are indoctrinated at some] religious school preaching extremism, who commit an act of terrorism just or shortly after being brainwashed and trained. [...] The persons who get indoctrinated through the internet and somehow find themselves a mentor, or self-train using the urban warfare training that Al Qaeda has made readily available on the internet can be classified in this terrorist group.

3) Sleepers: People in touch through family and friends’ connections with experienced terrorists, who act as their mentors and train them group wise. Sometimes sleepers are small groups embedded in the migrant settler community that due to incomplete integration into the host society may have hidden cells engaged in terrorist and criminal operations. Even the presence of mentor is not necessary in such situations. The police does not have enough proof (if there is some) to lock them up or list them as wanted publicity, albeit they may be monitoring some of them.

4) Known hard-core terrorists: People on the most wanted list of the FBI and other intelligence or law enforcement agencies.

With the focus on anti-terrorism in the West, the first type is increasingly being foiled at an early stage and with limited damage. However, the second category of terrorists is difficult to spot. For example, the London July 2005 bombers included a young Pakistani boy belonging to a good family, who unfortunately was influenced by some person(s) he met at a religious school. Or, the other example is of Mohammad Momin Khawaja, who was indoctrinated through the Internet and was arrested by the Canadian police after collaborating with the UK in March 2004, in connection with a large UK plot. He was a Canadian citizen, a contractual software operator in the Canadian Foreign Affairs Department, earning a decent income. He never got any formal training, however he was probably trained by Al Qaeda mentor [using the Internet.]

[p. 6]

2) New terrorists indoctrinated at some religious school preaching extremism who commit an act shortly after being brainwashed and trained. There are also persons who get indoctrinated through the internet and somehow find themselves a mentor and self-train using the urban-warfare training that al-Qaeda has made readily available on the internet.

3) “Sleepers” in touch through family and friends’ connections with experienced terrorists who act as their mentors and train them in small groups. Sometimes sleepers are small groups embedded in the migrant settler community that due to incomplete integration into the host society may have hidden cells engaged in terrorist and criminal activities with or without a mentor. The police have insufficient proof (if at all) to lock them up/ put them on a public black-list albeit they may be monitoring some of them.

[p. 7]

4) Known hardcore terrorists on the most wanted list of the FBI and other black-lists.

With the focus on anti-terrorism in the West, the first category is increasingly being foiled at an early stage with limited damage.

The second category of terrorists, for example, the recent London July 2005 bombers included a young Pakistani boy from a good family who unfortunately got swayed under the influence of some person(s) he met at a religious school. Or, the example of Mohammad Momin Khawaja indoctrinated through the internet, who was arrested by the Canadian police collaborating with the UK in March 2004, in connection with a large UK plot. He was a Canadian citizen of Pakistani origin, a contract computer software operator with the Canadian Foreign Affairs Department earning a decent income. He never got any formal training albeit he did manage to get himself an al-Qaeda mentor and probably trained himself using the internet.

 Anmerkungen continues from the previous page. After the first three paragraphs of this page it is not clear anymore that the source is still Bedi (2005), but also in the first three paragraphs there is text copied word-for-word without making that clear to the reader. Sichter (Graf Isolan), Hindemith

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[He never got any formal training, however he was probably trained by Al Qaeda mentor] using the Internet. Such cases are naturally tough to spot on, no matter how detailed any database is.

The third category of terrorists are for example, the Madrid bombers who may not have been on the US black-list at the time of Madrid Bombing, However, they are said to had ties to a ring of petty criminals that smuggled drugs and others who were involved in bank ATM fraud and robbery. Still, the masterminds were trained by Al Qaeda.

The fourth category is obviously the easiest to spot provided that identity theft and impersonations are detectable. The biggest problem with this category is that they would avoid completing transactions in their true names, that is, they would use identity theft to help them conceal their identities. People in this category include individuals who may have been to the Al Qaeda training camps in Afghanistan prior to 2001. For example, as many as 3,000 British born or based people are thought to have been trained in the camps and may since have trained others.

He never got any formal training albeit he did manage to get himself an al-Qaeda mentor and probably trained himself using the internet. Such cases are naturally tough to spot on any database.

The third category of terrorists are for example, the Madrid bombers who may not have been on the US OFAC black-list [...]. The Madrid Bombers had ties to a ring of petty criminals that smuggled drugs and others who were involved in bank ATM fraud and robbery. The masterminds were al-Qaeda trained.

The fourth category is obviously the easiest to spot albeit they would avoid doing transactions in their names ie, they would use identity theft to help them conceal their identities. People in this category include individuals who had been to the al-Qaeda training camps in Afghanistan prior to 2001. For example, as many as 3,000 British born or based people are thought to have been trained in the camps and may since have trained others.

 Anmerkungen continued from previous page. The source is given two pages further up, the reader would never assume that he is reading Bedi here instead of Nm. Sichter (Graf Isolan), Hindemith

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The problems of organized crime and terrorism were often considered separate phenomena prior to September 11. The security studies committee, the military and some parts of law enforcement increasingly viewed terrorism and transnational crime as distinct strategic threats. Seminars would discuss the emerging threat of transnational crime or terrorism but the important links between the two were rarely made (Shelley I. L., 2002).

2.3 TERRORISM AND ORGANIZED CRIME

The 9/11 attacks have changed the strategic thinking in this area. Terrorism and transnational crime are now considered as central threats to our national and international security. But, still more needs to be understood about the inter-linkage between the two [phenomena.]

The problems of organized crime and terrorism were often considered separate phenomena prior to September 11th. The security studies committee, the military and parts of law enforcement increasingly viewed terrorism and transnational crime as strategic threats. But these problems were often seen as distinct. Seminars would discuss the emerging threat of transnational crime or terrorism but the important links between the two were rarely made.

September 11th has changed the strategic thinking in this area. Terrorism and transnational crime are now central threats to our national and international security. Yet more needs to be understood about the links between these two phenomena.

 Anmerkungen Although the reference has been given by Nm the reader does not expect the paragraphs to be nearly identical since nothing has been marked as a citation. Sichter (Graf Isolan), Hindemith

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Mass media reports many illustrations of crime and terrorism link in Western Europe and the United States. However, much less has been written on this subject in the Pacific context. Illustrations of the links between organized crime and terrorism are the follows (Shelley I. L., 2002):

1) Terrorists engage in organized crime activity to raise funds and strengthen their financial position.

2) Both terrorists and organized crime groups often operate in form of networks. Sometimes these structures intersect, and thus terrorists can hide themselves among transnational criminal organizations

3) Both organized crime groups and terrorists operate in similar areas. The areas with weak governmental controls and enforcement of laws, and open borders are usually considered in this regard.

4) Both organized criminals and terrorists corrupt or use corrupt local officials to achieve their objectives

5) Organized crime and terrorists launder their money, often using the same methods and often the same operators to move their funds

6) Organized crime groups and terrorists often use similar means to communicate.

Many illustrations of the crime and terrorism link in Western Europe and the United States have appeared in the mass media. Much less has been written on this subject in the Pacific context. [...] Illustrations of the links between organized crime and terrorism are the following:

1) Terrorists engage in organized crime activity to support themselves financially

2) Organized crime groups and terrorists often operate on network structures and these structures sometimes intersect, terrorists can hide themselves among transnational criminal organizations

3) Both organized crime group and terrorists operate in areas with little governmental controls, weak enforcement of laws and open borders

4) Both organized criminals and terrorists corrupt local officials to achieve their objectives

5) Organized crime groups and terrorists often use similar means to communicate-exploiting modem technology

6) Organized crime and terrorists launder their money, often using the same methods and often the same operators to move their funds

 Anmerkungen Nm names his source as "Shelley I. L., 2002" - but the extent of the copying, especially with much word-for-word copying, is not made clear. Sichter (Graf Isolan), WiseWoman

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Transnational crime groups often operate on a network structure. The network structure is not unique to this group but characterizes many other terrorist groups and many of the new transnational organized crime groups. Unlike the top-down- hierarchical structure of a mafia-type organization, newer organized crime groups such as Russian-speaking groups and terrorist groups such as the Al Qaeda function as networks. It gives organizational flexibility, reduces the possibility of penetration and provides greater efficiency. These structures make it more difficult to identify leaders. The newer criminal groups and leading terrorist [organizations resemble more modern legitimate business structures than the older corporations like Ford and the steel industry.] Transnational crime groups often operate on a network structure. The network structure is not unique to this group but characterizes many other terrorist groups and many of the new transnational organized crime groups. Unlike the top-down-hierarchical structure of a mafia-type organization, newer organized crime groups such as Russian-speaking groups and terrorist groups such as the Al Qaeda function as networks. It gives organizational flexibility, reduces the possibility of penetration and provides greater efficiency. These structures make it more difficult to identify leaders. The newer criminal groups and leading terrorist organizations resemble more modern legitimate business structures than the older corporations like Ford and the steel industry.
 Anmerkungen Copy of the original - not marked as a citation. The source has been mentioned with respect to the foregoing list. Sichter (Graf Isolan), WiseWoman

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[The newer criminal groups and leading terrorist] organizations resemble more modern legitimate business structures than the older corporations like Ford and the steel industry.

Organized crime networks are often of the same nationality, though they form alliances with other foreign crime groups to conduct their activities. In contrast, the networks of groups such as Al Qaeda are considered themselves transnational. They can unite individuals from the Middle East with those in the Far East. Likewise, the organized crime groups in the Russian Far East work with North and South Koreans, Japanese, Chinese and Vietnamese among others. The networks are not usually affiliated with any particular state. They are non-state actors striking at the citizens of a state or different states.

The newer criminal groups and leading terrorist organizations resemble more modern legitimate business structures than the older corporations like Ford and the steel industry.

Organized crime group networks are often of the same nationality, though they form alliances with other crime groups to conduct their activities. In contrast, the networks of groups such as Al Qaeda are themselves transnational. They can unite individuals from the Middle East with those in the Far East. Likewise, the organized crime groups from the Russian Far East work with North and South Koreans, Japanese, Chinese and Vietnamese among others. The networks are not affiliated with any particular state. They are non-state actors striking at the citizens of a state, states or different states.

 Anmerkungen Continues the copying process from the previous page. Sichter (Graf Isolan), Bummelchen

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Terrorists, like transnational organized crime, use the information technology to maximize the effectiveness of their operations. For example the use of cellular and satellite telephones, the Internet, email and chat rooms are being used by them. The communication is facilitated with encryption and stenography (hiding messages within other messages). The anonymous interactive features of the Internet are used to evade detection. Whereas, organized criminals run gambling on the Internet, terrorists solicit funds through websites for charities that fund terrorist groups. International bank transfers and other fund movement technologies are also used by terrorists. Terrorists, like transnational organized crime, exploit information technology to maximize the effectiveness of their operations. They use cellular and satellite telephones, the Internet, email and chat rooms. They code their messages through encryption and steganography (hiding messages within other messages). They exploit the anonymyzer features of the Internet to evade detection. The Internet is a tool for perpetration of their crimes. Whereas, organized criminals run gambling on the Internet, terrorists solicit funds through websites for charities that fund terrorist groups. International fund movements are facilitated by information technology.
 Anmerkungen still copying from the same source - but this time with a mistake that confuses the meaning: "steganography" becomes "stenography" Sichter (Graf Isolan), Hindemith

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Members of a terrorist group are given special training on computers and software and computer engineers are hired by the groups to facilitate communications.

International organized crime groups and terrorists both employ specialists for specific purposes. These specialists conduct intelligence operations and target surveillance, move money, or perform information technology or communications specific. The most successful of these groups have educated specialists within their ranks while others contract out for these services. Their hiring is on contractual basis, they may know accomplices or may be [hired through intermediaries, unaware of the end goals and who the end users of their services are.]

Members of the terrorist group are provided special training in computers and software and computer engineers are hired by the groups to facilitate communications.

International organized crime groups and terrorists both employ specialists. These specialists conduct intelligence operations, move money, and specialize in information technology and communications. The most successful of these groups have educated specialists within their ranks while others contract out for these services. Those hired on a contractual basis may be knowing accomplices or may be hired through intermediaries, unaware who are the end users of their services.

 Anmerkungen still copying from Shelley (2002) Sichter (Graf Isolan), Hindemith

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[Their hiring is on contractual basis, they may know accomplices or may be] hired through intermediaries, unaware of the end goals and who the end users of their services are.

Criminals and terrorists both engage in illicit activities. Money is the main motivation for transnational criminals; whereas for terrorists, this ordinary criminal activity is supported by their larger political and ideological objectives. Yet the crimes committed by both of the groups differ only in motive and not in substance.

Those hired on a contractual basis may be knowing accomplices or may be hired through intermediaries, unaware who are the end users of their services. [...]

Criminals and terrorists both engage in illicit activity. The transnational criminals do this solely to make money. Whereas for terrorists, this ordinary criminal activity is used to support their larger political and ideological objectives. Yet the crimes committed by these two groups differ only in motive and not in substance.

 Anmerkungen Still continuing the copying-process. The source is not given. Sichter (Graf Isolan), Hindemith

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The organizational structure determines the strengths and weaknesses of a group. Knowledge about prevalent models of terrorist organizations leads to a better understanding of their capabilities and targets. Knowledge of the different labels and systems of classification that have been applied to groups and individuals aid in discarding useless or irrelevant terms, and in understanding the purposes and usefulness of different terminologies.

Traditionally, a specific political agenda, ideological motivation or the desire for national or ethic liberation dominates the understanding of terrorism. Although, the discussed image is true for a number of terrorist organizations, but it is no longer universally valid. Also, a generational change in leadership of wellknown groups is in many cases ushering in a more damaging and relentless type of organization.

There are two general categories of terrorist organizations: networked and hierarchical. Newer organizations tend to employ the networked model, while strict Leninist or Maoist group tending towards hierarchical model to exercise centralized control.

The organizational structure of a group determines its strengths and weaknesses. A general knowledge of the prevalent models of terrorist organizations leads to a better understanding of their capabilities. Knowledge of the different labels and systems of classification that have been applied to groups and individuals aid us in discarding useless or irrelevant terms, and in understanding the purposes and usefulness of different terminologies.

In recent times, the popular image of a terrorist group operating according to a specific political agenda and motivated by ideology or the desire for ethnic or national liberation dominated our understanding of terrorism. While still true of some terrorist organizations, this image is no longer universally valid. Also, a generational change in leadership of established groups is in many cases ushering in a more a destructive and relentless type of organization.

There are two general categories of organization; hierarchical and networked. [...] Newer groups tend towards organizing or adapting to the possibilities inherent in the network model. [...] strict Leninist or Maoist groups tending towards centralized control and hierarchical structure.

 Anmerkungen Fairly minor changes. The source is not mentioned anywhere in the thesis. Sichter (Hindemith), WiseWoman

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[Since in the case of larger structure, nearly all organizations follow the] variants of cellular organizations at strategic and tactical level to enhance security. It also facilitates better management and organization of operations.

Terror groups often require political activity and hierarchical structure to coordinate violence with a political action. It may also be necessary for a politically affiliated group to observe cease-fire agreements or avoid particular targets in support of political objectives. This can be difficult to enforce in networked organizations.

Terrorist groups can be at various stages of development in terms of capabilities and sophistication. Newer groups having fewer resources will usually lack capability and experience, and operate in permissive areas or under the control of more proficient organizations. Change in terrorist leadership may signal significant adjustments to organizational priorities and means of conducting terrorism. The terrorist groups associated with ethnic or nationalist agendas operating in one country or a localized region tend to require fewer capabilities as compared to larger groups. Larger groups can coalesce from smaller organizations, or smaller groups can splinter off from larger ones.

2.5 TERRORIST GROUP STRUCTURE

Members or supporters of terrorist organization can be classified into four types based on their level of commitments: passive supporters, active supporters, cadre, and leadership. Figure 2.1 shows how each successive level of commitment has fewer members. This pyramid diagram shows the relative number of people in each category and not the organizational structure. It is valid for either network or hierarchical organizational structure. Passive supporters may mix together with active supporters and are not aware of their actual relationship with the organization.

Within the larger structure, though, virtually all groups use variants of cellular organizations at the tactical level to enhance security and to organize for operations.

Terrorist groups that are associated with a political activity or organization will often require a more hierarchical structure, in order to coordinate terrorist violence with political action. It also can be necessary for a politically affiliated group to observe cease-fire agreements or avoid particular targets in support of political objectives. This can be difficult to enforce in networked organizations.

Terrorist groups can be at various stages of development in terms of capabilities and sophistication. Newer groups with fewer resources will usually be less capable, and operate in permissive areas or under the tutelage of more proficient organizations to develop proficiency. Change in terrorist leadership, [...] may signal significant adjustments to organizational priorities and means of conducting terrorism. Also, groups professing or associated with ethnic or nationalist agendas and limiting their operations to one country or a localized region tend to require fewer capabilities. Larger groups can coalesce from smaller organizations, or smaller groups can splinter off from larger ones.

[p. 3-2]

Section I: Terrorist Group Structure

[...]

There are typically different levels of commitment within an organization: passive supporters, active supporters, cadre, and leadership. Figure 3-1 shows how each successive level of commitment has fewer members. This pyramid diagram is not intended as an organizational picture, but to show the relative number of people in each category. This image of overall density holds true for networks as well as hierarchies. Passive supporters may intermingle with active supporters and be unaware of what their actual relationship is to the organization.

 Anmerkungen The only mention of the source is in connection with the citation of figure 2.1 on the next page (page 59). The reader is left in the dark about the origin of the text. Sichter (Graf Isolan), Hindemith

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• Passive supporters are motivated by the announced goals of the terrorist organization. They may have ideological sympathy with the terrorist organization, however they are not committed enough to take any action. Passive supporters are often unaware of their real relation with the terrorist

organization. However, they are used for political activities, fund raising campaigns, gathering assisting in gathering intelligence and other nonviolent activities. Sometimes fear of reprisal from terrorists is a compelling factor in passive support.

• Passive Supporters are typically individuals or groups that are sympathetic to the announced goals and intentions of the terrorist organization, but are not committed enough to take action. They may not be aware of their precise relation to the terrorist group, and interface with a front that hides the overt connection to the terrorist group. Sometimes fear of reprisal from terrorists is a compelling factor in passive support. Sympathizers can be useful for political activities, fund raising, and unwitting or coerced assistance in intelligence gathering or other non-violent activities.
 Anmerkungen continuation from previous page - the source is only mentioned as reference for figure 2.1. on the same page, but not as reference for anything else. On this and the next page, Nm will present more or less word-for-word the description of each of the four "levels of commitment", which can be found in the source, by using large chunks of the original wording and putting it together with slightly different "stuffing". Another "major" change he will introduce is the permutation of the order of the various descriptions ("bottum-up" instead of "top-down"). Sichter (Graf Isolan), Hindemith

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• Active Supporters actively participate in the political, fundraising, and information activities of the group. They may also conduct initial intelligence and surveillance activities, and provide safe houses, financial contributions, medical assistance, and transit assistance for active members of the organization. They do not commit or engage them in violence, but are usually fully aware of their relationship to the terrorist group and motives of the organization. • Active Supporters are active in the political, fund-raising, and information activities of the group. Acting as an ally or tacit partner, they may also conduct initial intelligence and surveillance activities, and provide safehaven houses, financial contributions, medical assistance, and transit assistance for active members of the organization. They are usually fully aware of their relationship to the terrorist group but do not commit violent acts.
 Anmerkungen continues the list of descriptions; the source is not mentioned Sichter (Graf Isolan), Hindemith

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Mid-level cadres tend to be trainers and technicians such as bomb makers, financiers, and surveillance experts. Low-level cadres are the bombers and similar direct actors in terrorism and violent plans. Mid-level cadres tend to be trainers and technicians such as bomb makers, financiers, and surveillance experts. Low-level cadres are the bombers and similar direct action terrorists in an attack.
 Anmerkungen continued from the previous page; still no mention of the source. Sichter (Graf Isolan), Hindemith

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• Leaders design organizational policy and provide directions. They approve goals and objectives and provide management and guidance for operations. Usually leaders rise from within the ranks of any given organization, or create their own organization from scratch. • Leaders provide direction and policy; approve goals and objectives; and provide overarching guidance for operations. Usually leaders rise from within the ranks of any given organization, or create their own organization from scratch.
 Anmerkungen this finishes the list - still no mention of the source Sichter (Graf Isolan), Hindemith

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Terrorist groups will recruit from the population having sympathy to their goals. Often legitimate organizations can serve as recruiting grounds for terrorists. For example: Militant Islamic recruiting is often associated with the proliferation of the radical Wahhabi sect. The recruitment takes place via Wahhabist schools worldwide, financed from both governmental and private donations and grants (Corpus N. Victor, 2002). In the time of need, particular skills or qualification is also considered during recruitment. Of particular concern are attempts of terrorist organizations to recruit current or former members of the armed forces, both as trained operatives, and as agents in place.

Recruitment can gain operatives from many diverse social backgrounds. At times, the approach to radical behaviour or direct actions with terrorism can develop over the course of years or decades. One example is John Walker Lindh. Lindh was the U.S. citizen, captured by U.S. military forces during the war in Afghanistan. His notoriety jumped into international attention, as did the situation of individuals from several counties that were apprehended in combat actions of Afghanistan. Lindh was changed from an unassuming middle-class adolescent in the Western United States to a member of a paramilitary training camp in Pakistan and his subsequent support for Taliban forces in Afghanistan spotlights that general profiling should be tempered with specific instances [and a broad perspective.]

[p. 3-2]

Terrorist groups will recruit from populations that are sympathetic to their goals. Often legitimate organizations can serve as recruiting grounds for terrorists. Militant Islamic recruiting, for example, is often associated with the proliferation of the radical Wahhabi sect. This recruiting is conducted on a worldwide basis via Wahhabist schools financed from both governmental and non-governmental donations and grants. [FN 128] Some recruiting may be conducted for particular skills and qualifications, and not be tied to ideological characteristics. Of particular concern are attempts of terrorist organizations to recruit current or former members of the U.S. armed forces, both as trained operatives, and as agents in place.

[FN 128] Victor N. Corpus, “The Invisible Army” (Briefing presented at Fort Leavenworth, KS, 5 November 2002), TRADOC ADCSINT-Threats Files, Fort Leavenworth, KS.

[p. 3-3]

Recruitment can gain operatives from many diverse social backgrounds. At times, the approach to radical behavior or direct actions with terrorism can develop over the course of years or decades. One example is John Walker Lindh, the U.S. citizen captured by U.S. military forces in the war in Afghanistan. His notoriety jumped into international attention, as did the situation of individuals from several counties that were apprehended in combat actions of Afghanistan. Lindh’s change from an unassuming middle-class adolescent in the Western United States to a member of a paramilitary training camp in Pakistan and subsequent support for Taliban forces in Afghanistan spotlights that general profiling should be tempered with specific instances and a broad perspective.

 Anmerkungen copying continues without any break - no mention of the source. Sichter (Graf Isolan), Hindemith

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Another stunning case was of Jose Padilla. He attempted very simple and voluntary efforts to detonate a bomb in the U.S. This illustrates Al Qaeda techniques to support, finance, and use less sophisticated means to conduct terrorist acts.

Some groups will also use coercion and leverage to gain limited or one-time cooperation from useful individuals. Blackmailing and intimidation are the commonly exerted coercion forms by terrorist organizations to gain cooperation of useful individuals. This cooperation can range anywhere like acquiring the useful information to conduct a suicide bombing operation (Reich Walter, 1998). Threats to family members are also employed. Coercion is often directed at personnel in government security and intelligence organizations.

In the case of Jose Padilla, his simple and voluntary efforts to detonate a bomb in the U.S. may illustrate al Qaeda techniques to support, finance, and use less than sophisticated means to conduct terrorist acts.

[FIGURE]

Some groups will also use coercion and leverage to gain limited or one-time cooperation from useful individuals. This cooperation can range anywhere from gaining information to conducting a suicide bombing operation. [FN 129] Blackmail and intimidation are the most common forms of coercion. Threats to family members are also employed. Coercion is often directed at personnel in government security and intelligence organizations.

[FN 129] Walter Reich, ed., Origins of Terrorism: Psychologies, Ideologies, Theologies, States of Mind, rev. ed. (Washington: Woodrow Wilson Center Press, 1998), 270-271.

 Anmerkungen continued from previous page - no mention of the source is made - some slight change in the order of sentences. Sichter (Graf Isolan), Hindemith

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2.6 TACTICAL-LEVEL CELLULAR ORGANIZATION

The smallest elements at the tactical level of terrorist organizations are the cells that serve as building-blocks for the terrorist organization. One of the primary reasons for a cellular or compartmentalized structure is security. The compromise or loss of one cell should not compromise the identity, location, or actions of other cells. A cellular organizational structure makes it difficult for an adversary to penetrate the entire organization. Personnel within one cell are often unaware of the existence of other cells and, therefore, cannot divulge sensitive information to infiltrators or captors.

The Earth Liberation Front (ELF) is an excellent example of the cellular organization. The homepage ELF site states that ELF is modelled after Animal Liberation Front in the structured way to maximize the effectiveness. Operating in cells not only guarantees the security of group members but also such decentralized structure [helps to continue conducting actions.]

Tactical-level Cellular Organization

The smallest elements at the tactical level of terrorist organizations are the cells that serve as building blocks for the terrorist organization. One of the primary reasons for a cellular or compartmentalized structure is security. The compromise or loss of one cell should not compromise the identity, location, or actions of other cells. A cellular organizational structure makes it difficult for an adversary to penetrate the entire organization. Personnel within one cell are often unaware of the existence of other cells and, therefore, cannot divulge sensitive information to infiltrators or captors. The home page of the Earth Liberation Front is an excellent example of this cellular organization. It states, “Modeled after the Animal Liberation Front, the E.L.F. is structured in such a way as to maximize effectiveness. By operating in cells (small groups that consist of one to several people), the security of group members is maintained. [...] This decentralized structure helps keep activists out of jail and free to continue conducting actions.”

 Anmerkungen Minimal to no adjustments in the beginning. Later on Nm diverges from the original - but only a little bit. The source is not mentioned at all. The passage continues on the next page. Sichter (Hindemith), Graf isolan, WiseWoman

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[Operating in cells not only guarantees the security of group members but also such decentralized structure] helps to continue conducting actions.

Terrorists may organize cells based on family or employment relationships, on a geographic basis, or by specific functions such as direct action and intelligence. Terrorist groups may also form multifunctional cells and these cells may be used by terrorist groups to control its members. Cell members remain in close contact with each other in order to provide emotional support and to prevent desertion or breach of security procedures. Cell leaders are normally the people who communicate and coordinate with higher levels and other cells.

A terrorist group may form only one cell or may form many cells that operate locally, trans-nationally, or internationally. The composition, size and number of cells in the terrorist organization depend on the size of the terrorist group itself. An international terrorist group operating in more than within one country has more cells than the one operating in a single country or limited territory.

This decentralized structure helps keep activists out of jail and free to continue conducting actions.”

Terrorists may organize cells based on family or employment relationships, on a geographic basis, or by specific functions such as direct action and intelligence. The terrorist group may also form multifunctional cells. The terrorist group uses the cells to control its members. Cell members remain in close contact with each other in order to provide emotional support and to prevent desertion or breach of security procedures. The cell leader is normally the only person who communicates and coordinates with higher levels and other cells.

A terrorist group may form only one cell or may form many cells that operate locally, transnationally, or internationally. The number of cells and their composition depend on the size of the terrorist group. A terrorist group operating within one country frequently has fewer cells and specialized teams than does an international terrorist group that may operate in several countries.

 Anmerkungen Minimal adjustments. The source is not mentioned. The portion taken from the source starts in the middle of a quotation from the home page of the Earth Liberation Front Sichter (Hindemith), Graf isolan, WiseWoman

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2.7 GROUP ORGANIZATIONAL STRUCTURE

As stated earlier, there are two basic models used when examining the overall organizational structure of a terrorist group. These are the hierarchical and the networked models. A terrorist group may employ either type or a combination of the two models.

2.7.1 Hierarchical Structure

Hierarchical structure organizations maintain a well-defined vertical chain of command, authority and responsibility. Data and intelligence flows up and down organizational channels that correspond to these non-horizontal chains, but may not move horizontally through the organization. This is more traditional, and is common of groups that are well established with a command and support structure.

Group Organizational Structure

As stated earlier, there are two basic models used when examining the overall organizational structure of a terrorist group. These are the hierarchical and the networked models. A terrorist group may employ either type or a combination of the two models.

Hierarchical Structure

Hierarchical structure organizations are those that have a well-defined vertical chain of command linkage and responsibility. Data and intelligence flows up and down organizational channels that correspond to these vertical chains, but may not move horizontally through the organization. This is more traditional, and is common of groups that are well established with a command and support structure.

 Anmerkungen text nearly identical, but the source is not mentioned. Sichter (Graf Isolan), Hindemith

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Hierarchical structure offers the organizations greater specialization of functions in their subordinate cells (support, operations, intelligence). Usually, the leader of cell is aware of other cells or contacts of the organization (may be to a limited extent) and only senior leadership has visibility of the organization at large. In the past, terrorism was practiced in this manner by identifiable organizations with a command and control structure influenced by ideology or theory of revolution. Radical leftist organizations such as the Japanese Red Army, the Red Army Faction in Germany, the Red Brigades in Italy, as well as ethno-nationalist terrorist movements such as the Palestine Liberation Organization, the Irish Republican Army and the Basque separatist ETA group, conformed to this stereotype of the "traditional" terrorist group. These organizations had a clearly defined set of political, social or economic objectives, and tailored aspects of their organizations (such as a “political” wing or “social welfare” group) to facilitate their success. The necessity to coordinate actions between various “fronts,” some of which were political and allegedly non-violent, and the use of violence by terrorists and some insurgents, favoured a hierarchical command structure.

2.7.2 Networked Structure

Terrorists are in this decade become increasingly part of far more joint and wider system of networks than experienced before. Groups based on religious or single-issue motives lack a specific political or patriotic agenda; non-horizontal structure is thus less needed. Instead, they can depend and even thrive on loose association with like-minded clusters or people from a diversity of places. General objectives and goals are announced, and operation and initiative is left to the individuals or cells.

Hierarchical organizations feature greater specialization of functions in their subordinate cells (support, operations, intelligence). Usually, only the cell leader has knowledge of other cells or contacts, and only senior leadership has visibility of the organization at large. In the past, terrorism was practiced in this manner by identifiable organizations with a command and

[Page 3-5]

control structure influenced by revolutionary theory or ideology. Radical leftist organizations such as the Japanese Red Army, the Red Army Faction in Germany, the Red Brigades in Italy, as well as ethno-nationalist terrorist movements such as the Palestine Liberation Organization, the Irish Republican Army and the Basque separatist ETA group, conformed to this stereotype of the "traditional" terrorist group. These organizations had a clearly defined set of political, social or economic objectives, and tailored aspects of their organizations (such as a “political” wing or “social welfare” group) to facilitate their success. The necessity to coordinate actions between various “fronts,” some of which were political and allegedly nonviolent, and the use of violence by terrorists and some insurgents, favored a strong and hierarchical authority structure.

Networked Structure

Terrorists are now increasingly part of far more indistinct and broader system of networks than previously experienced. Groups based on religious or single-issue motives lack a specific political or nationalistic agenda; they therefore have less need for a hierarchical structure to coordinate the achievement of their goals. Instead, they can depend and even thrive on loose affiliation with like-minded groups or individuals from a variety of locations. General goals and targets are announced, and individuals or cells are expected to use flexibility and initiative to conduct the necessary action.

 Anmerkungen The source is not mentioned. Sichter (Hindemith), WiseWoman

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[FIGURE, identical to source]

Figure 2.2. Typical Categories of Terrorist Organization

[FIGURE]

Figure 3-2. Typical Categories of Terrorist Organization

 Anmerkungen The figure in the thesis has been taken via copy-paste from the source, except for the last pixel line, which was part of the frame. The source is not given. Sichter (Hindemith), WiseWoman

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2.8 DIMENSIONS OF CRIMINAL/ TERRORIST NETWORKS

Although networks are an important, and somewhat neglected, form of criminal organization, they are not the single or exclusive system. The traditional non-horizontal model long associated with Mafia families in the U.S., for example, does not need to be abandoned: After all, it is possible to have networks of hierarchies, hybrid organizational forms with some hierarchical mechanisms and a substantial network aspect, and even a network of networks. The shapes and sizes of the networks can be diverse; however, they vary along several critical scopes.

First, a network can be created and directed by a core of coordinators/ organizers who want to use it for specific purposes (a “directed network”) or it can emerge spontaneously as a mechanism to add efficiency to the functioning of a market (a [“business network”).]

Dimensions of Criminal Networks

Although networks are an important, and somewhat neglected, form of criminal organization, they are not the sole or exclusive form. The traditional hierarchical model long associated with Mafia families in the United States, for example, does not need to be jettisoned: After all, it is possible to have networks of hierarchies, hybrid organizational forms with some hierarchical components and a significant network dimension, and even a network of networks. If networks come in a great variety of shapes, however, they vary along several critical dimensions.

First, a network can be created and directed by a core of organizers who want to use it for specific purposes (a “directed network”) or it can emerge spontaneously as a mechanism to add efficiency to the functioning of a market (a “transaction network”).

 Anmerkungen Slight adaptations. The source is not given. Sichter (Hindemith), WiseWoman

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The Colombian cocaine trade can be seen as an example of directed network. Its core members at least came into being to carrying of cocaine to U.S. in the 1980s and early 1990s and directed the whole network to achieve the objective. Whereas, the heroin trade from Southeast Asia, in contrast, is far more of a business network, in which agents (we may call them brokers) play a critical role at almost every stage of the process. Herion [sic!] reaches the retail market by passing through a sequence of brokers/ agents and independent suppliers are responsible for moving it from manufacturers to agents. In practice, a directed network can be part of a larger business network, and it seems that with the demise of the large, vertically cohesive networks functioning out of Medellín and Cali, the Colombian cocaine trade has increasingly taken on this hybrid value.

Second, networks can range from small, inadequate relations at the local level to transnational provider networks responsible for moving both legal and illegal belongings across national borders. Membership can be determined by a particular characteristic, such as ethnicity, or can be relatively open. The networks are likely to be multi-ethnic when influential considerations balance the need to maintain a high degree of selectiveness.

Among the larger criminal networks, it is possible to identify both key individuals and key corporations or firms through which they operate. One of the best examples of a widespread transnational criminal network is that revolving around Semeon Mogilevich. Based in Hungary, Mogilevich is reputed to have close links with the Solntsevo criminal organization in Moscow, with prostitution activities in Frankfurt, with the Genovese family in New York, and with Russian criminals in Israel. For several years, Mogilevich operated in part through a company called Magnex YBM operating in the United States and Canada. The company was engaged in money laundering and stock frauds. It also had a network of companies in the Bahamas, the British Channel Islands, and the [Caymans.]

The Colombian cocaine trade in the 1980s and early 1990s was very much a directed network—at least at the core—which came into existence to transport cocaine to the United States. The heroin trade from Southeast Asia, in contrast, is far more of a transaction network, in which brokers play a critical role at almost every stage of the process. Producers supply heroin to independent distributors, and it is then passed along a chain of brokers until it reaches the retail market. In practice, of course, a directed network can be part of a larger transaction network, and it appears that with the demise of the large, vertically integrated networks operating out of Medellin and Cali, the Colombian cocaine trade has increasingly taken on this hybrid quality.

Second, networks can range from small, very limited associations at the local level to transnational supplier networks that move a variety of goods, either licit or illicit—or even both—across national borders. Membership can be determined by a particular characteristic, such as ethnicity, or can be relatively open. Supplier networks are likely to be multiethnic when instrumental considerations outweigh the desire or need to maintain a high degree of exclusiveness.

Among the larger criminal networks, it is possible to identify both key individuals and key companies or firms through which they operate. One of the best examples of an extensive transnational criminal network is that revolving around Semeon Mogilevich. Based in Hungary,

[Page 70]

Mogilevich is reputed to have close links with the Solntsevo criminal organization in Moscow, with prostitution activities in Frankfurt, with the Genovese family in New York, and with Russian criminals in Israel. For several years, Mogilevich operated in part through a company called Magnex YBM operating in the United States and Canada (where it was engaged in money laundering [...] and stock fraud) and also had a network of companies in the Bahamas, the British Channel Islands, and the Caymans.

 Anmerkungen Slight adaptations. The source is not referenced. Sichter (Hindemith), WiseWoman

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In such situation, Mogilevich was far less vulnerable than the leader of traditional Mafia group, in spite of its important role in such transnational network. Despite of continued claims about his role, he has never been sentenced of any corruption/ crime.

Third, networks can be highly structured and enduring in nature orthey can be loose, fluid, or imprecise in character, with members coming and going according to particular requirements, prospects, and/ or demands. Some individuals or even small organizations will point in and out of networks when it is convenient for them to do so. Other networks will have a more enduring membership. In yet other cases, there will be some members who provide continuity and direction to the network, while others will play an irregular or transitory part. There will be both “embedded ties” and continuing relations based on high levels of trust, mutual respect, and mutual concern; but also more transitory connections based on nothing more than a short-term chance of interests. An analogous dynamism is obvious in the way in which criminals develop businesses to make maximum of prospects and closing them whenever is needed or when they are directed in those particular areas.

Last, networks can be motivated barely on a sole purpose or on the supply of a particular product, or they can supply a broader range of illegitimate goods or engage in more diverse criminal activities. For example Colombian and Mexican drug trafficking organizations do not engage themselves into a wide range of activities. Although there has been a tendency to traffic in more than one kind of drug, essentially they are in the drug trafficking business and little else. On the other hand Russian and Chinese criminal organizations are involved in very diverse criminal activities like drug trafficking, dealing in lifted/ stolen cars and weapons, prostitution, antiquities, and vanishing classes, yet also engaging in numerous systems of extortion and even in monetary [fraud.]

Significantly, as a key figure in this transnational network, Mogilevich is far less vulnerable than a traditional Mafia don or family head, and, despite continued allegations about his role, he has never been convicted of any crime.

Third, networks can be highly structured and enduring in nature or they can be loose, fluid, or amorphous in character, with members coming and going according to particular needs, opportunities, and demands. Some individuals or even small organizations will drift in and out of networks when it is convenient for them to do so. Other networks will have a more enduring membership. In yet other cases, there will be some members who provide continuity (and direction) to the network, while others will play an occasional or ephemeral part. There will be both “embedded ties” and enduring relations based on high levels of trust, mutual respect, and mutual concern; but also more fleeting relations based on nothing more than a shortterm coincidence of interests. A similar dynamism is evident in the way in which criminals develop and use front companies, creating them wherever opportunities exist and abandoning or closing them whenever they become the targets of law enforcement investigations.

Last, networks can be focused very narrowly on a single purpose or on the supply of a single product, or they can supply a broader range of illegal products or engage in more diverse criminal activities. Colombian and Mexican drug trafficking organizations, for example, engage in a very narrow range of activities. Although there has been a tendency to traffic in more than one kind of drug, essentially they are in the drug trafficking business and little else. Russian and Chinese criminal organizations, in contrast, have a very diverse portfolio of criminal activities, trafficking in drugs, stolen cars, arms, prostitution, antiqui-

[Page 71]

ties, and endangered species, yet also engaging in various forms of extortion and financial fraud.

 Anmerkungen Somewhat adjusted. Source is not referenced. Sichter (Hindemith), WiseWoman

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Networks with their particular characteristics offer substantial eyecatching choices to terrorists. For example, range, flexibility, low visibility, durability, etc. are well known some of the attractive characteristics.

Networks can often operate clandestinely. The more visible a criminal enterprise the more likely it is to be attacked by law enforcement. It is well known fact that “Networks are not immediately visible”, which is considered strongly by criminal enterprises and can be used to hide behind various licit activities. Those can operate with a lower degree of formality than other types of organization, and can maintain a profile that does not bring them to the attention of law enforcement. In some cases, of course, the network will be exposed. However, in the beginning of the investigation of Mogilevich network by FBI, there was considerable surprise at its extensiveness.

Even when they are targeted by law enforcement, many criminal networks are inherently dispersed, with the result that they do not provide obvious centres of gravity or loci for law enforcement attacks. Lacking a physical substructure or a large investment of sunk costs that would add significantly to their weakness, networks can also migrate easily from zones where risks from law enforcement are maximum to zones where the risks are much lesser.

Criminal networks, especially when they are transnational in character, can exploit differences in national laws and regulations. For example Russian criminals travelled to Israel during 1990’s, which was lacking in money laundering laws and measures. Israel criminalized money laundering in 2000. In some cases, money from Russia was used in Israel to buy up virtually bankrupt businesses that would then start to make “profits” that flowed back to Russia. In some instances transnational criminal organizations also create jurisdictional confusion, making it difficult for any [single nation’s law enforcement agencies to act effectively against them.]

Whatever their precise characteristics, networks provide criminals with diversity, flexibility, low visibility, durability, and the like. Indeed, their attractions are very considerable:
• Networks can often operate clandestinely. The more visible a criminal enterprise the more likely it is to be attacked by law enforcement. One of the most significant points about networks, however, is that they are not immediately and obviously visible. Criminal networks can hide behind various licit activities, can operate with a lower degree of formality than other types of organization, and can maintain a profile that does not bring them to the attention of law enforcement. In some cases, of course, the network will be exposed. Significantly, though, when the FBI began to investigate the Mogilevich criminal network, there was considerable surprise at its extensiveness.
• Even when they are targeted by law enforcement, many criminal networks are inherently dispersed, with the result that they do not provide obvious centers of gravity or loci for law enforcement attacks. Lacking a physical infrastructure or a large investment of sunk costs that would add significantly to their vulnerability, networks can also migrate easily from areas where risks from law enforcement are high to areas where the risks are much lower.
• Criminal networks, especially when they are transnational in character, can exploit differences in national laws and regulations (Israel, for example, only criminalized money laundering in 2000) by engaging in what might be termed jurisdictional arbitrage. Throughout the 1990s, for example, criminals from the former Soviet Union flooded into Israel, exploiting both the law of return and the lack of anti-money laundering measures. In some cases, money from Russia was used in Israel to buy up virtually bankrupt businesses that would then start to make “profits” that flowed back to Russia. In some instances transnational criminal organizations also create jurisdictional confusion, making it difficult for any single nation’s law enforcement agencies to act effectively against them.
 Anmerkungen Somewhat adjusted. Source is not referenced. In the first sentence "criminals" is replaced by "terrorist" and like that the focus of the section is adapted to what is needed for the thesis. In the third paragraph the American spelling ("center") is anglicized. Sichter (Hindemith), WiseWoman

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[In some instances transnational criminal organizations also create jurisdictional confusion, making it difficult for any] single nation’s law enforcement agencies to act effectively against them. Laundering money through a series of firms and banks in multiple jurisdictions, for example, makes it arduous and costly for law enforcement to follow up the money trail.

Networks also offer opportunities for both redundancy and flexibility. In network structures, it is easier to create redundancies than it is in more formal and rigid organizations. It can operate, even when a part of same network is destroyed, at the same time it can become very resilient and can be easily rebuilt.

In view of these benefits, it is not unexpected that network structures have become particularly predominant in modern organized crime, whether in the United States, Europe, or states in transition such as Russia, Ukraine, other newly independent states of the former Soviet Union, South Africa, and Cambodia, or even China and Cuba. As reported in the referenced book, the analysis now looks at the main characteristics of criminal networks; characteristics that help make them extremely difficult to combat.

In some instances transnational criminal organizations also create jurisdictional confusion, making it difficult for any single nation’s law enforcement agencies to act effectively against them. Laundering money through a series of firms and banks in multiple jurisdictions, for example, makes it arduous and costly for law enforcement to follow the money trail.

[Seite 72]

• Networks also offer opportunities for both redundancy and resilience. In network structures, it is easier to create redundancies than it is in more formal and rigid organizations — so that even if part of the network is destroyed it can still operate. Furthermore, degradation of a network does not necessarily lead to its demise: Networks are very resilient and can easily be rebuilt.

In view of these advantages, it is not surprising that network structures have become particularly prevalent in contemporary organized crime, whether in the United States, Europe, or states in transition such as Russia, Ukraine, other newly independent states of the former Soviet Union, South Africa, and Cambodia, or even China and Cuba. Accordingly, the analysis now looks at the main characteristics of criminal networks, characteristics that help make them extremely difficult to combat.

 Anmerkungen Some adjustments. The source has not been referenced Sichter (Hindemith), WiseWoman

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2.9 TERRORIST CHARACTERISTICS

Terrorists do not have a single or general personality profile. Meaning, no single analytical test exist which promises empathy of a terrorist. Although, many terrorism related studies have been conducted by analyzing the biographical and social data on known terrorists, with goal to develop some form of terrorist profile. However, none of them truly succeeded as they have just shown that in general, terrorists are people who often feel isolated from society and have a complaint or regard themselves as victims of an inequality.

Political or religious reasons help as a commitment to the terrorists and they do not regard their violent actions as criminal. They show no pity or regret for their actions. Although their level of [complexity will vary depending on the individual and the specific terrorist group, terrorists are people who are skilled and brutal in leading terrorist acts (Hudson, A. R, 1999).]

Section III: Terrorist Characteristics

No singular personality profile of a terrorist exists, and no predictive test exists that can guarantee identification of a terrorist. Numerous terrorism-related studies have analyzed the biographical and social data on known terrorists in an attempt to develop some form of terrorist profile. Studies have shown that in general, terrorists are people who often feel alienated from society and have a grievance or regard themselves as victims of an injustice. They are devoted to their political or religious cause and do not regard their violent actions as criminal, showing no pity or remorse for their actions. Although their level of sophistication will vary depending on the individual and the specific terrorist group, terrorists are people who are skillful and ruthless in conducting terrorist acts.[FN 115]

[FN 115] Rex A. Hudson, The Sociology and Pshychology[sic] of Terrorism: Who Becomes a Terrorist and Why? (Washington: Library of Congress Federal Research Division, 1999), 50.

 Anmerkungen With the source never mentioned, the whole of section III of the source is to be found - mostly word-for-word - in the thesis under scrutiny. This is the starting-point. The source misspells a word in Hudson's book, Nm corrects this word in the bibliography but does not give the year. Sichter (Graf isolan), WiseWoman

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[Although their level of] complexity will vary depending on the individual and the specific terrorist group, terrorists are people who are skilled and brutal in leading terrorist acts (Hudson, A. R, 1999). In addition to the above qualities, there are some general characteristics that are equally common among terrorists. There are also some common stereotypes and misconceptions regarding terrorists. Although their level of sophistication will vary depending on the individual and the specific terrorist group, terrorists are people who are skillful and ruthless in conducting terrorist acts. [FN 115] In addition to the above traits, there are some general characteristics that are fairly common among terrorists. There are also some common stereotypes and misperceptions regarding terrorists.

[FN 115] Rex A. Hudson, The Sociology and Pshychology of Terrorism: Who Becomes a Terrorist and Why? (Washington: Library of Congress Federal Research Division, 1999), 50.

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Some authors, particularly Sparrow (1991), Coles (2001), Klerks (2001) and Williams (2001) have identified a certain number of characteristics of criminal networks but these characteristics are either very general, for social networks, or much more specific for criminal networks. The main characteristics are presented below: Some authors, particularly Sparrow (1991), Coles (2001), Klerks (2001) and Williams (2001), have identified a certain number of characteristics of criminal networks and have demonstrated how they can be analyzed. These characteristics are either very general, for social networks, or much more specific for criminal networks. We will present the main characteristics, [...]
 Anmerkungen Some adjustments, but all content including four literature references can also be found in the source, which is not referenced. Sichter (Hindemith) Agrippina1

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2.9.1 Status

Terrorists belong to middle or extremely wealthy background; opposite to common understanding that terrorist are sufferers of poverty and despair. While guerrilla fighters and gang members often come from poor and disadvantaged backgrounds, and may adopt terrorism as a tactic. According to the study conducted by Marc Sageman, a Senior Fellow at PFRI and a former CIA case officer in Afghanistan, out of 400 Islamic terrorists 75% came from the upper or middle class and 90% came from caring, intact families (Sagman, M, 2004). The less educated and socially dispossessed people may be used to conduct acts of terrorism. Even in terrorist groups that espouse the virtues of “the people” or “the proletariat,” leadership consists primarily of those of middle class backgrounds. However, this characteristic must be considered in context with the originating society “Middle class” and “privilege” are relative term. Both mean completely different levels of income between Western Africa and Western Europe.

2.9.2 Education and Intellect

Generaly Terrorists are educated to more than average level, except [very few Western terrorists, which are uneducated or illiterate (Hudson, A. R, 1999).]

[p. 2-12]

Status

Contrary to the oft-repeated charge that terrorism is a product of poverty and despair, terrorists are most commonly from middle class backgrounds, with some actually coming from extreme wealth and privilege. While guerilla fighters and gang members often come from poor and disadvantaged backgrounds, and may adopt terrorism as a tactic, terrorist groups that specifically organize as such generally come from middle and upper social and economic strata. Marc Sageman, a Senior Fellow at PFRI and a former CIA case officer in Afghanistan, conducted a study of 400 Islamic terrorists. He found that 75% came from the upper or middle class and 90% came from caring, intact families. [FN 116] The leadership may use less educated and socially dispossessed people to conduct acts of terrorism. Even in terrorist

[FN 116] Marc Sageman, “Understanding Terror Networks,” 3.

[p. 2-13]

groups that espouse the virtues of “the people” or “the proletariat,” leadership consists primarily of those of middle class backgrounds. However, this characteristic must be considered in context with the society the terrorist originates from. “Middle class” or “privilege” are relative terms and will, for example, mean completely different levels of income between Western Africa and Western Europe.

Education and Intellect

Terrorists in general have more than average education, and very few Western terrorists are uneducated or illiterate.[FN 117]

[FN 117] Rex A. Hudson, The Sociology and Psychology of Terrorism: Who Becomes a Terrorist and Why?, 48.

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[Generaly Terrorists are educated to more than average level, except] very few Western terrorists, which are uneducated or illiterate (Hudson, A. R, 1999). Some core members of larger terrorist organizations may have minimal education, but this characteristic is not the standard. Left wing terrorists, international terrorists, and the leadership echelon of right wing groups are usually of average or better intelligence, and have been exposed to advanced education. In fact, terrorist groups are increasingly recruiting members with expertise in areas such as communications, computer programming, engineering, finance, and the sciences (Hudson, A. R, 1999). The study of Sageman revealed 63% of his group had gone to college and 75% of those were professionals or semi-professionals (Sagman, M, 2004): For example, Osama Bin Laden is a civil engineer; Ayman Zawahiri is a physician. These terrorists generally have had exposure to higher learning, although they are usually not highly intellectual, and are frequently dropouts or possess poor academic records. Again, this is subject to the norms of the society they originate from. In societies where religious fundamentalism is prevalent, the higher education may have been advanced religious training (Harmon, C. Christopher, 2001).

Domestic and right wing terrorists in general belong to lower educational and social levels, although they are not completely uneducated. The right wing domestic groups in the U.S. first explored the organizational and communication potential of the Internet. They will typically have received a high school level education. They were also well versed and indoctrinated in the ideological arguments they support.

2.9.3 Age

Terrorists tend to be young. The terrorists which take part in operations are found to be within age group of 20-35, while the leaders, supporters or training cadres range from 40-50 years old. (Lacquer Walter, 1999). The amount of practical experience and [training that contributes to making an effective operative is not usually present in individuals younger than the early 20s.]

Terrorists in general have more than average education, and very few Western terrorists are uneducated or illiterate. [FN 117] Some leaders of larger terrorist organizations may have minimal education, but this characteristic is not the norm. Left wing terrorists, international terrorists, and the leadership echelon of right wing groups are usually of average or better intelligence, and have been exposed to advanced education. In fact, terrorist groups are increasingly recruiting members with expertise in areas such as communications, computer programming, engineering, finance, and the sciences. [FN 118] The Sageman analysis reflected 63% of his group had gone to college and three-quarters were professionals or semi-professionals. [FN 119] (Usama bin laden a civil engineer; Ayman Zawahiri a physician; and Yasir Arafat was at one time a civil engineer.) These terrorists generally have had exposure to higher learning, although they are usually not highly intellectual, and are frequently dropouts or possess poor academic records. Again, this is subject to the norms of the society they originate from. In societies where religious fundamentalism is prevalent, the higher education may have been advanced religious training. [FN 120]

Domestic and right wing terrorists tend to come from lower educational and social levels, although they are not uneducated. It was right wing domestic groups in the U.S. that first explored the communication and organizational potential of the Internet. They will typically have received a high school level education, and be very well indoctrinated in the ideological arguments they support.

Age

Terrorists tend to be young. Leadership, support, and training cadres can range into the 40-50 year old age groups, but most operational members of terrorist organizations are in the 20-35 year old age group.[FN 121] The amount of practical experience and training that contributes to making an effective operative is not usually present in individuals younger than the early 20s.

[FN 117] Rex A. Hudson, The Sociology and Psychology of Terrorism: Who Becomes a Terrorist and Why?, 48.

[FN 118] Ibid., 4.

[FN 119] Marc Sageman, “Understanding Terror Networks,” 3.

[FN 120] Christopher C. Harmon, Terrorism Today (London: Frank Cass Publishers, 2000; reprint, Portland: Frank Cass Publishers, 2001), 208.

[FN 121] Walter Lacquer, The New Terrorism: Fanaticism and the Arms of Mass Destruction (New York: Oxford University Press, 1999), 38.

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[The amount of practical experience and] training that contributes to making an effective operative is not usually present in individuals younger than the early 20s.

Individuals in their teen-age have been employed as soldiers in guerrilla groups, but terrorist organizations usually do not tend to accept extremely young members, although they will use them as non-operational supporters or suicide bombers. Groups that utilize suicide operations will employ very young individuals as suicide assets, but these youths are not actually members of the organization, but are simply exploited or coerced into an operational role (Reich Walter, 1998). Many countries in the developing world subjected to ethnic, political, and religious violence; however, are seeing younger members being recruited by terrorist organizations. Pre-teens and adolescents are often receptive to terrorist recruiting because they have witnessed killings and see violence as the only way to deal with grievances (Hudson, A. R, 1999).

[p. 2-13]

The amount of practical experience and training that contributes to making an effective operative is not usually present in individuals younger than the early 20s. Individuals in their teens have been employed as soldiers in guerilla groups, but terrorist organizations do not tend to accept extremely young members, although they will use them as non-operational supporters. Groups that utilize suicide operations will employ very young individuals as suicide assets, but these youths are not actually members of the organization, but simply exploited or coerced into an operational role. [FN 122] Many countries in the developing

[FN 122] Walter Reich, ed., Origins of Terrorism: Psychologies, Ideologies, Theologies, States of Mind, rev. ed. (Washington: Woodrow Wilson Center Press, 1998), 270.

[p. 2-14]

world subjected to ethnic, political, and religious violence; however, are seeing younger members being recruited by terrorist organizations. Pre-teens and adolescents are often receptive to terrorist recruiting because they have witnessed killings and see violence as the only way to deal with grievances. [FN 123]

[FN 123] Rex A. Hudson, The Sociology and Psychology of Terrorism: Who Becomes a Terrorist and Why?, 48.

 Anmerkungen continued from previous page - the source is not mentioned. Sichter (Graf Isolan), Hindemith

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2.9.4 Gender

Terrorists are not exclusively male. Usually Women’s roles will often be constrained to support or intelligence and surveillance work, but some fundamentalist Islamic groups use women in operational roles. In groups where religious constraints do not affect women’s roles, female membership may be above 50%, with women fully integrated into operations. Female leadership of terrorist groups is not uncommon and female terrorists do not lay behind male counterparts in terms of violence and ruthlessness. For example, one-third of the Liberation Tiger of Tamil Eelam (LTTE) cadre is made up of women and it is reported that nearly 4,000 have been killed since they began taking part in combat in 1985, over 100 of those killed belonging to the dreaded Black Tiger suicide squad (http://www.eelam.com/ltte).

In August 2004, female Chechen suicide bombers were responsible for detonating improvised explosive devices (IEDs) while on [Russian commercial flights that resulted in two aircraft crashes and the death of all people on board.]

Gender

Terrorists are not exclusively male, even in groups that are rigorously Islamic. Women’s roles in these groups will often be constrained to support or intelligence work, but some fundamentalist Islamic groups use women in operational roles. In groups where religious constraints do not affect women’s roles, female membership may be above fifty percent, with women fully integrated into operations. Female leadership of terrorist groups is not uncommon, and female terrorists lack for nothing in terms of violence and ruthlessness. For example, one-third of the LTTE cadre is made up of women and it is reported that nearly 4,000 have been killed since they began taking part in combat in 1985, over 100 of those killed belonging to the dreaded Black Tiger suicide squad. [FN 125]

In August 2004, female Chechen suicide bombers were responsible for detonating IEDs while on Russian commercial flights that resulted in two aircraft crashes and the death of all people on board.

[FN 124] 124 “Liberation Tigers of Tamil Eelam (LTTE),” South Asia Terrorism Portal, n.d., 2; available from http://www.satp.org/satporgtp/ countries/shrilanka/ terroristoutfits/Ltte.htm; Internet; accessed 7 July 2004.

[FN 125] Ibid., 2.

 Anmerkungen Only minimal changes Sichter (Graf Isolan), WiseWoman

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[In August 2004, female Chechen suicide bombers were responsible for detonating improvised explosive devices (IEDs) while on] Russian commercial flights that resulted in two aircraft crashes and the death of all people on board. Within one week, another female Chechen suicide bomber detonated an IED near a metro station in northeast Moscow causing extensive property damage and injuring many people in the area (Alfano, B., 2004). However, female participation and leadership is less common in some right wing groups, particularly those with neo-Nazi and Christian Identity oriented ideologies.

2.9.5 Appearance

Terrorist usually do not appear out of the ordinary, and are capable of normal social behaviour and appearance. Thus, terrorists are often unremarkable in individual characteristics. Racial diversity in organizations such as Al Qaeda signal that attempts to racially profile likely terrorist group members is not an effective indicator. Over the long term, elements of fanatical behaviour or ruthlessness may become evident, but they are typically not immediately obvious to casual observation. An excellent example of this is the group 17 November in Greece. When the police captured 14 suspected members in 2002, the most striking characteristic was their ordinary nature. Among the group there was a school teacher, a shopkeeper, a telephone operator, and other members that appeared to be members of mainstream society. Most terrorists do not marry, even though there have been some examples of married couples within terrorist organizations. Although members of sleeper cells or other hidden operators may marry as part of their persona,

[p. 2-14]

In August 2004, female Chechen suicide bombers were responsible for detonating IEDs while on Russian commercial flights that resulted in two aircraft crashes and the death of all people on board. Within one week, another female Chechen suicide bomber detonated an IED near metro station in northeast Moscow causing extensive property damage and injuring many people in the area. [FN 126]

Again, there is an exception to this general observation in some right wing groups, particularly those with neo-Nazi and Christian Identity oriented ideologies. Female participation and leadership is much less common in these groups.

Appearance

Terrorists are often unremarkable in individual characteristics. Racial diversity in organizations such as al Qaeda signal that attempts to racially profile likely terrorist group members is not an effective indicator. They usually do not appear out of the ordinary, and are capable of normal social behavior and appearance. Over the long term, elements of fanatical behavior or ruthlessness may become evident, but they are typically not immediately obvious to casual observation. An excellent example of this is the group 17 November in Greece. When the police captured 14 suspected members in 2002, the most striking characteristic was their ordinary nature. Among the group were a schoolteacher, a shopkeeper, a telephone operator, and other members that appeared to be members of

[FN 126] Billy Alfano, Briefing: “Terrorism Strikes Russia, Summary of the Attacks from August 24 to September 3, 2004,” Department of state, Diplomatic Security, Overseas Security Advisory Council, International Security Specialist for Western Europe, n.d.

[p. 2-15]

mainstream society. [FN 127] Although members of sleeper cells or other covert operators may marry as part of their persona, most terrorists do not marry, even though there have been cases of married couples within terrorist organizations.

[FN 127] “Revolutionary Organization 17 November (17N),” CDI Terrorism Project, 5 August 2002; available from http://www.cdi.org/terrorism/17N-pr.cfm; Internet; accessed 24 September 2004.

 Anmerkungen continuation from previous page; Nm not only copies word-for-word, but follows exactly the line of argumentation found in the source - thus a passage on "Appearance" follows a passage on "Gender". The last sentence in the sub-chapter ends with a comma that is also found in the source, but the second half of the sentence was not copied. Sichter (Graf Isolan), WiseWoman

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2.9.6 Network Cores

Networks of any significant size will generally have a central / core and a periphery/ foot soldier, reflecting asymmetries of power, influence, and status within the network. The central member is characterized by dense connections among individuals who, in the case of a directed network, provide the steering mechanism for the [network as a whole.]

Network Cores

Networks of any substantial size will generally have both a core and a periphery, reflecting asymmetries of power, influence, and status within the network. The core is characterized by dense connections among individuals who, in the case of a directed network, provide the steering mechanism for the network as a whole.

 Anmerkungen Slight adaptations. The source is not referenced Sichter (Hindemith), WiseWoman

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Usually the originators of the criminal enterprise, the central members initiate specific criminal activities, arbitrate disputes, and provide direction. Their relationship is often supported by bonding mechanisms that help to create high degrees of trust and cohesion.

In many cases, bonding will be directly related to family or kinship: For example, many Italian Mafia groups are still organized along family lines, while Turkish drug trafficking and criminal organizations are often clan based. Other bonding mechanisms include ethnicity and common experience in which the participants develop a strong sense of trust and mutual reliance.

Membership in youth gangs or time spent together in prison can also provide critical bonding mechanisms. In the United States, the Mexican Mafia (which is not actually Mexican) started as a prison gang in Southern California but has developed much more extensively. Yet, it is the common experience that continues to provide the network core with the capacity to operate with confidence and believe that disloyalty or defection is very unlikely.

If network cores exhibit strong collective identities, cohesion does not necessarily enhance—and can actually reduce—the capacity to obtain information and “mobilize resources from the environment.” Indeed, recent trends in network analysis posit an inverse relationship, in general, between the density/intensity of the coupling of network ties on the one hand and their openness to the outside environment on the other (Grabher, G., Stark, D., 1997)

This explains the attraction of a two-tier structure in which the weaknesses of the central member in carrying out the functions of information acquisition are more than offset by the foot soldiers/ peripheries.

2.9.7 Network Peripheries

The peripheries feature less dense patterns of interaction and looser [relationships than the core.]

Usually the originators of the criminal enterprise, the core members initiate specific criminal activities, arbitrate disputes, and provide direction. Their relationship is often underpinned by bonding mechanisms that help to create high degrees of trust and cohesion.

In many cases, bonding will be directly related to family or kinship: Many Italian Mafia groups are still organized along family lines, while Turkish drug trafficking and criminal organizations are often clan based. Other bonding mechanisms include ethnicity and common experience in which the participants develop a strong sense of trust and mutual reliance.

Membership in youth gangs or time spent together in prison can also provide critical bonding mechanisms. In the United States, the Mexican Mafia (which is not actually Mexican) started as a prison gang in

[Page 73]

Southern California but has developed much more extensively. Yet, it is the common experience that continues to give the core of the network a capacity to operate with confidence that disloyalty or defection are unlikely.[FN 15]

If network cores exhibit strong collective identities, cohesion does not necessarily enhance—and can actually reduce—the capacity to obtain information and “mobilize resources from the environment.” Indeed,

recent trends in network analysis posit an inverse relationship, in general, between the density/intensity of the coupling of network ties on the one hand and their openness to the outside environment on the other.[FN 16]

This explains the attraction of a two-tier structure in which the weaknesses of the core in carrying out the functions of information acquisition are more than offset by the periphery.

Network Peripheries

This zone features less dense patterns of interaction and looser relationships than the core.

[FN 15] The analysis here and the discussion of bonding mechanisms rests heavily on Ianni, 1974, pp.282-293.

[FN 16] See David Stark and Gernot Grabher, “Organizing Diversity: Evolutionary Theory, Network Analysis, and Postsocialist Transformations,” in Stark and Grabher, eds., Restructuring Networks: Legacies, Linkages, and Localities in Postsocialism (New York and London: Oxford University Press, in press).

 Anmerkungen Very minor adaptations. The source is not given. One literature reference has been removed, another reference for a quote has been taken from the source, and the period was omitted after the inserted reference. Sichter (Hindemith), WiseWoman

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Yet, these characteristics play a critical role in networks, exhibiting and exploiting “the strength of weak ties” (Granovetter, M., 1973). In effect, the periphery allows the network to operate at a far greater distance, both geographically and socially, than would otherwise be the case. Thus it facilitates more-extensive operations, more-diverse activities, and the capacity to carry out effective intelligence collection. Yet, these characteristics play a critical role in networks, exhibiting and exploiting “the strength of weak ties.” [FN 17] In effect, the periphery allows the network to operate at a far greater distance —both geographically and socially— than would otherwise be the case, facilitating more-extensive operations, more-diverse activities, and the capacity to carry out effective intelligence collection. [FN 18]

[FN 17] Mark Granovetter, “The Strength of Weak Ties,” American Journal of Sociology, Vol. 78 (1973) pp. 1360-1380.

[FN 18] Ibid. and Burt, 1992.

 Anmerkungen Only very slight adaptations. The source is not given. Note that also one literature reference is taken from the source (and another has been removed). Note also the identical usage of the words "more-extensive" and "more-diverse". Sichter (Hindemith), WiseWoman

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2.9.8 Size of Networks

Size is a fundamental characteristic of networks. It determines many other characteristics including link density, commonly called as density of the network.

Generally, density is higher in a small network than in a large one. It is because in a large network, a large proportion of connections between participants are indirect.

This is the case for transnational criminal networks, which were specifically studied by Williams and his collaborators (Williams, 2001; Williams and Godson, 2002). Williams states that these networks can be considered to be composed of strategic alliances between national networks, e.g., the Columbian drug trade network and the Sicilian drug distribution network.

There are also large networks within a single country. Generally, they are made up of subset of the main networks among which there are loose couplings through weak ties, particularly important in criminal networks.

2.9.9 Redundancy in Networks

The link density of a network increases, if several actors or ties must be removed to break it into unconnected pieces making it more redundant. As stated by Williams (2001) “…redundancy enables members of the network to take over tasks and responsibilities from those who have been arrested, incarcerated, or [killed by law enforcement.”]

Size of the Networks

Size is a fundamental characteristic of networks, in that it determines many other characteristics, particularly the density of the networks.

[...]

Generally, density is higher in a small network than in a large one, meaning that, in a large network, a large proportion of connections between participants are indirect.

This is the case for transnational criminal networks, which were specifically studied by Williams and his collaborators (Williams, 2001; Williams and Godson, 2002). Williams states that these networks can be considered to be composed of strategic alliances between national networks, e.g., the Columbian drug trade network and the Sicilian drug distribution network.

There are also large networks within a single country. Generally, they are made up of subnetworks between which there are loose couplings through weak ties, particularly important in criminal networks.

[...][Page 12]

A network is even more redundant and, thus, denser, if several actors or ties must be removed to break it into pieces that are not connected. As stated by Williams (p. 81) “ … redundancy enables members of the network to take over tasks and responsibilities from those who have been arrested, incarcerated, or killed by law enforcement.”

 Anmerkungen Minor Adjustments. The source is not given anywhere in the thesis. Sichter (Hindemith) Agrippina1

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First, incomplete, incorrect, or inconsistent data can create problems. Moreover, these characteristics of terrorist networks cause difficulties:

-Incompleteness. Criminal networks are clandestine networks that work in concealment and secrecy (Krebs, 2002). Criminals may reduce communications to avoid attracting attention of law enforcement agencies and their communications are concealed behind a number of illegal activities. Therefore, data about criminal networks is certainly treated as incomplete; that is, some existing links or nodes will be overlooked or unrecorded (Sparrow, M. K., 1991).

-Incorrectness. Many criminals hide their identity (provide incorrect information to the agencies) when they are captured and under investigation. Incorrect data regarding criminals’ identities, physical characteristics, and addresses may result either from accidental data entry errors or from intentional cheating by criminals.

-Inconsistency. Information about criminals, who have captured a number of times at number of places, may be entered in law [enforcement databases multiple times.]

First, incomplete, incorrect, or inconsistent data can create problems. Moreover, these characteristics of criminal networks cause difficulties not common in other data mining applications:

Incompleteness[EN 10]. Criminal networks are covert networks that operate in secrecy and stealth [EN 8]. Criminals may minimize interactions to avoid attracting police attention and their interactions are hidden behind various illicit activities. Thus, data about criminals and their interactions and associations is inevitably incomplete, causing missing nodes and links in networks [EN 10].

Incorrectness. Incorrect data regarding criminals’ identities, physical characteristics, and addresses may result either from unintentional data entry errors or from intentional deception by criminals. Many criminals lie about their identity information when caught and investigated.

Inconsistency. Information about a criminal who has multiple police contacts may be entered into law enforcement databases multiple times.

[EN 8] Krebs, V. E. Mapping networks of terrorist cells. Connections 24, 3 (2001), 43–52.

[EN 10] Sparrow, M.K. The application of network analysis to criminal intelligence: An assessment of the prospects. Social Networks 13 (1991), 251–274.

 Anmerkungen Starts out as "word-for-word paraphrasing" and becomes "word-for-word copying" (even more obvious on the next page). The source is not given If indeed there is an article where "Most part of the contents of this subsection is already published in (Memon N., and Larsen H. L., 2006a).", as Nm claims in the footnote, one has to wonder about the refereeing process there, too. Sichter (Graf Isolan), Bummelchen

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[Information about criminals, who have captured a number of times at number of places, may be entered in law] enforcement databases multiple times. These records are not unsurprisingly consistent. It would be found very strange if multiple data records could make a single criminal appear to be different individuals. When apparently different individuals are included in a network under investigation, misleading information may be produced.

The literature study found that the problems particularly to criminal network analysis lie in data transformation, fuzzy boundaries, and network dynamics.

-Data Transformation. Network analysis requires that data be presented in a particular format, in which (network) members’ represent nodes, their communications are represented by links. Though, information about criminal relations is typically not precise in raw data and converting them to the required format can be known as laborious and time-consuming.

-Fuzzy boundaries. The boundaries of criminal networks are relatively confusing. [...] Therefore, it is found during the literature review that it can be very tough for an analyst to choose whom to include and whom to exclude from a network under investigation (Sparrow, M. K., 1991).

-Dynamic. Criminal networks are known as dynamic networks, that is, they usually to change over time. {The relationship between any two individuals binary nature, it means there is a relation or there is no relation, it may be weak or strong; rather it has a distribution over time, waxing and waning from one period to another. It is also noted that most of relations change in time.} Therefore, it is need of the time to design and develop new methods of data collection in order to capture the dynamics of criminal networks (Sparrow, M. K., 1991).

Information about a criminal who has multiple police contacts may be entered into law enforcement databases multiple times. These records are not necessarily consistent. Multiple data records could make a single criminal appear to be different individuals. When seemingly different individuals are included in a network under study, misleading information may result.

Problems specific to criminal network analysis lie in data transformation, fuzzy boundaries, and network dynamics:

Data transformation. Network analysis requires that data be presented in a specific format, in which network members are represented by nodes, and their associations or interactions are represented by links. However, information about criminal associations is usually not explicit in raw data. The task of extracting criminal associations from raw data and transforming them to the required format can be fairly labor-intensive and time-consuming.

• Fuzzy boundaries. Boundaries of criminal networks are likely to be ambiguous. It can be quite difficult for an analyst to decide whom to include and whom to exclude from a network under study [EN 10].

• Network dynamics. Criminal networks are not static, but are subject to changes over time. New data and even new methods of data collection may be required to capture the dynamics of criminal networks [EN 10].

 Anmerkungen Text between line 25 and line 29 (marked by {}) seems to be Nm's own. It is left as an example of Nm's style in comparison to the original own. (These lines are not counted). Sichter (Graf Isolan), Bummelchen

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Criminal Network Analysis, which is a broad category of terrorist network analysis, can be categorized into three generations (Xu J., Chen H., 2006):

First generation: Manual approach. The representation of first generation is known as Anacapa chart (Klerks, 2001). Using this approach, an analyst should first develop an association matrix by detecting criminal associations from raw data. Then, a link chart for visualization purposes can then be drawn based on the association matrix. For example, to map the terrorist network containing the 19 hijackers in 9/11 attacks, Krebs (Krebs, 2002) gathered data about the relationships among the hijackers from publicly available information reported in several major newspapers. Krebs then manually constructed an association matrix to incorporate these relations (Krebs, 2002) and illustrated a network representation in order to analyze the structural properties of the network.

It is well known fact that such a manual approach for criminal network analysis is helpful in crime investigation; but this type of approach is would be good if the dataset is short, but it would be difficult rather impossible to draw a link chart if there are thousands of nodes.

Second generation: Graphic-based approach. These tools can automatically produce graphical representations of criminal networks. Most of the available network analysis tools belong to this generation. Among them Analyst’s Notebook, Netmap and XANALYS Link Explorer (previously called Watson) are the most popular (Xu, J., Chen H., 2006). It is to mention that, Analyst’s Notebook (see Figure 2.3) can automatically generate a link chart [based on relational data from a spread sheet or text file.]

Klerks [EN 7] categorized existing criminal network analysis approaches and tools into three generations.

First generation: Manual approach. Representative of the first generation is the Anacapa Chart [EN 6]. With this approach, an analyst must first construct an association matrix by identifying criminal associations from raw data. A link chart for visualization purposes can then be drawn based on the association matrix. For example, to map the terrorist network containing the 19 hijackers in the September 11 attacks, Krebs [EN 8] gathered data about the relationships among the hijackers from publicly released information reported in several major newspapers. He then manually constructed an association matrix to integrate these relations [EN 8] and drew a network representation to analyze the structural properties of the network (Figure 1).

Although such a manual approach for criminal network analysis is helpful in crime investigation, it becomes an extremely ineffective and inefficient method when data sets are very large.

Second generation: Graphic-based approach. These tools can automatically produce graphical representations of criminal networks. Most existing network analysis tools belong to this generation. Among them Analyst’s Notebook [EN 7], Netmap [EN 5], and XANALYS Link Explorer (previously called Watson) [EN 1], are the most popular. For example, Analyst’s Notebook can automatically generate a link chart based on relational data from a spreadsheet or text file (Figure 2a).

[EN 1] Anderson, T., Arbetter, L., Benawides, A., and Longmore-Etheridge, A. Security works. Security Management 38, 17, (1994), 17–20.

[EN 5] Goldberg, H.G., and Senator, T.E. Restructuring databases for knowledge discovery by consolidation and link formation. In Proceedings of 1998 AAAI Fall Symposium on Artificial Intelligence and Link Analysis. AAAI Press (1998).

[EN 6] Harper, W.R., and Harris, D.H. The application of link analysis to police intelligence. Human Factors 17, 2 (1975), 157–164.

[EN 7] Klerks, P. The network paradigm applied to criminal organizations: Theoretical nitpicking or a relevant doctrine for investigators? Recent developments in the Netherlands. Connections 24, 3 (2001), 53–65.

[EN 8] Krebs, V. E. Mapping networks of terrorist cells. Connections 24, 3 (2001), 43–52.

 Anmerkungen Source is given at the beginning, but nothing has been marked as a citation. Again "Most of the parts of the contents of this subsection are already published in (Memon N., and Larsen H. L., 2006a)." Sichter (Graf Isolan), Bummelchen

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[It is to mention that, Analyst’s Notebook (see Figure 2.3) can automatically generate a link chart] based on relational data from a spread sheet or text file.

Though second generation tools are capable of using a number of methods to visualize criminal networks, their sophistication level is not up to the mark because they only produce graphical representation of criminal networks with less analytical functionality. These tools still rely on analysts to investigate the graphs with awareness to detect structural properties of the network.

[...]

Third generation: SNA. This generation provides more advanced functionality to help the law enforcement professionals in crime investigations. Complex structural analysis tools are needed to visualization facility in addition of mining large amount of data in order to discover useful knowledge about the structure and organization of criminal networks.

For example, Analyst’s Notebook can automatically generate a link chart based on relational data from a spreadsheet or text file (Figure 2a).

[...]

Although second-generation tools are capable of using various methods to visualize criminal networks, their sophistication level remains modest because they produce only graphical representations of criminal networks without much analytical functionality. They still rely on analysts to study the graphs with awareness to find structural properties of the network.

Third generation: SNA. This approach is expected to provide more advanced analytical functionality to assist crime investigation. Sophisticated structural analysis tools are needed to go from merely drawing networks to mining large volumes of data to discover useful knowledge about the structure and organization of criminal networks.

 Anmerkungen continuation from previous page Sichter (Graf Isolan), Bummelchen

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A terrorist network is primarily a social network in which individuals connect with one another through various connections such as kinship, friendship, colleagues, and classmates, etc. Research has recognized SNA as a promising methodology to analyze the structural properties of criminal/ terrorist networks (Krebs, V., 2002; McAndrew, D., 1999 Sparrow, M.K., 1991). SNA was originally used in sociology research to extract patterns of relationships between social actors in order to discover the underlying social structure (Wasserman, S. and K. Faust, 1994; Wellman, B., 1988). A social network is often known as a graph in which nodes (or actors) represent individual members and links (or connections) represent relations among the members. The sructural properties of a social network can be described and analyzed at four levels: node, link, group, and overall network. SNA provides various measures, indexes, and approaches to capture these structural properties quantitatively. A criminal network is primarily a social network in which individuals connect with one another through various relations such as kinship, friendship, and co-workers. Research has recognized SNA as a promising methodology to analyze the structural properties of criminal networks [EN 23, EN 26, EN 35]. SNA was originally used in sociology research to extract patterns of relationships between social actors in order to discover the underlying social structure [EN 38, EN 39]. A social network is often treated as a graph in which nodes represent individual members and links represent relations among the members. The structural properties of a social network can be described and analyzed at four levels: node, link, group, and the overall network. SNA provides various measures, indexes, and approaches to capture these structural properties quantitatively.

---

[EN 23] Krebs, V.E. (2001). Mapping networks of terrorist cells. Connections, 24(3), 43-52.

[EN 26] McAndrew, D. (1999). The structural analysis of criminal networks, in The social psychology of crime: Groups, teams, and networks, offender profiling series, iii, D. Canter & L. Alison (eds.). Aldershot: Dartmouth.

[EN 35] Sparrow, M.K. (1991). The application of network analysis to criminal intelligence: An assessment of the prospects. Social Networks, 13, 251-274.

[EN 38] Wasserman, S. & K. Faust (1994). Social network analysis: Methods and applications, ed. Series. Cambridge: Cambridge University Press.

[EN 39] Wellman, B. (1988). Structural analysis: From method and metaphor to theory and substance, in Social structures: A network approach, B. Wellman & S.D. Berkowitz (eds.). Cambridge University Press: Cambridge.

 Anmerkungen Only minor adaptations. All literature references have been copied as well. The source is not given Sichter (Hindemith), Graf Isolan, WiseWoman

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Researchers (for example; Simmel, 1906; Gross, 1980; Geis and Stotland, 1980; Erickson, 1981; Baker and Faulkner, 1993; Klerks, 2001) have conducted psychological and sociological analyses of covert networks for the past century, however since Sept. 11, 2001 there has been a dramatic increase in the number of publications (ex. Krebs, 2001; Carley et al, 2003, Sageman, 2004) on covert networks, specifically terror networks. These recent researchers have chosen Social Network Analysis (SNA) to help them “map,” (Krebs, 2001) “uncloak,” (Krebs, 2002) “identify key players,” (Borgatti, 2002) “destabilize,” (Carley et al., 2003) and “understand” (Sageman, 2004) terror networks. Researchers (ex. Simmel, 1906; Gross, 1980; Geis and Stotland, 1980; Erickson,

1981; Baker and Faulkner, 1993; Klerks, 2001) have conducted analyses of clandestine networks for the past century, however since Sept. 11, 2001 there has been a dramatic increase in the number of publications (ex. Krebs, 2001; Carley et al, 2003, Sageman, 2004) on clandestine networks, specifically terror networks. These recent researchers have chosen SNA to help them "map," (Krebs, 2001) "uncloak," (Krebs, 2002) "identify key players," (Borgatti, 2002) "destabilize," (Carley et al., 2003) and "understand" (Sageman, 2004) terror networks.

 Anmerkungen No source given Sichter (Hindemith) Agrippina1

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There have been some empirical studies that have used SNA methods to analyze criminal or terrorist networks. For instance, based on archival data, Baker and Faulkner analyzed the structure of an illegal network depicting a price-fixing conspiracy in the heavy electrical equipment industry. Their findings supported that individual centrality in the network, as measured by degree, betweenness, and closeness (Freeman, L.C., 1979), was an important forecaster of an individual’s possible prosecution (Baker, W.E. and R.R. Faulkner 1993). Krebs analysed the open source data and studied the terrorist network involved in 9/11 terrorist plot. He found that Mohamed Atta, who piloted the first plane that crashed into the World Trade Center, had the highest degree and acted as the ring leader of the network (Krebs, V., 2002). Xu and Chen employed clustering, centrality measures, block-modeling,and multidimensional scaling (MDS) approaches from SNA to study criminal networks based on crime incident data (Xu, J. & H. Chen, 2003). The system they developed can also visualize a network and its groups.

In the following section we review related SNA research about dynamic network analysis and visualization.

2.13 ANALYZING SOCIAL NETWORK DYNAMICS

Recently, the attention on research on social network dynamics has increased. However, there has not been a consensus on what analytical methods to use (Carley, K.M., et al., 2003; Doreian, P., et al., 1997; Nakao, K. and A.K. Romney, 1993). Research uses various methods, measures, models, and techniques to studynetwork dynamics. Doreian and Stokman classified existing approaches into three categories: descriptive, statistical, andsimulation methods (Doreian, P. and F.N. Stokman, 1997).

There have been some empirical studies that use SNA methods to analyze criminal or terrorist networks. For instance, based on archival data, Baker and Faulkner analyzed the structure of an illegal network depicting a price-fixing conspiracy in the heavy electrical equipment industry. They find that individual centrality in the network, as measured by degree, betweenness, and closeness [EN 17], is an important predictor of an individual’s possible prosecution [EN 1]. Krebs relied on open source data and studied the terrorist network centering around the 19 hijackers in 9/11 events. He found that Mohamed Atta, who piloted the first plane that crashed into the World Trade Center, had the highest degree and acted as the ring leader of the network [EN 23]. Xu and Chen employed clustering, centrality measures, blockmodeling, and

[P. 4]

multidimensional scaling (MDS) approaches from SNA to study criminal networks based on crime incident data [EN 41]. The system they developed can also visualize a network and its groups.

[...]

3. Literature Review

In this section we review related SNA research about dynamic network analysis and visualization.

3.1 Analyzing social network dynamics

Recently, the research on social network dynamics has received increasing attention. However, there has not been a consensus on what analytical methods to use [EN 4, EN 14, EN 27]. Research uses various methods, measures, models, and techniques to study network dynamics. Doreian and Stokman classified existing approaches into three categories: descriptive, statistical, and simulation methods [EN 15].

[Pp. 27-29]

[EN 1] Baker, W.E. & R.R. Faulkner (1993) The social organization of conspiracy: Illegal networks in the heavy electrical equipment industry. American Sociological Review, 58(12), 837-860.

[EN 4] Carley, K.M., et al. (2003) Destabilizing dynamic covert networks. In Proceedings of the 8th International Command and Control Research and Technology Symposium. Washington DC., VA.

[EN 14] Doreian, P., et al. (1997) A brief history of balance through time, in Evolution of social networks, P. Doreian & F.N. Stokman (eds.). Gordon and Breach: Australia. 129-147.

[EN 15] Doreian, P. & F.N. Stokman (1997) The dynamics and evolution of social networks, in Evolution of social networks, P. Doreian & F.N. Stokman (eds.). Gordon and Breach: Australia. 1-17.

[EN 17] Freeman, L.C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1, 215-240.

[EN 23] Krebs, V.E. (2001). Mapping networks of terrorist cells. Connections, 24(3), 43-52.

[EN 27] Nakao, K. & A.K. Romney (1993) Longitudinal approach to subgroup formation: Re-analysis of Newcomb's fraternity data. Social Networks, 15, 109-131.

[EN 41] Xu, J. & H. Chen (2003). Untangling criminal networks: A case study. In Proceedings of NSF/NIJ Symposium on Intelligence and Security Informatics (ISI'03) Tucson, AZ.

 Anmerkungen Source is not given. The text has been copied with only minor adjustments -- also all literature references have been copied. Sichter (Hindemith), WiseWoman

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2.13.1 Descriptive methods

The descriptive analysis is often employed to detect structural changes in social networks. Desciptive methods are used to test how well a sociologic theory is supported by empirical data. With descriptive methods, structural properties of a social network are measured by various metrics and indexes and compared across time to describe the dynamics in nodes, links, or groups in the network. Little research has been found that studied the dynamics at the overall network level.

Node level measures and values often focus on and reflect the changes in individuals’ centrality, influence, and other characteristics in a social network. To study how an individual’s social position relates to his or her technology adoption behavior, Burkhardt and Brass studied a communication network of 94 employees of an organization at four time points after a new computerized information system was deployed (Burkhardt, M.E. and D.J. Brass, 1990).

They found that the centrality (degree and closeness) and power of early adopters of new technology increased over time. Using Newcomb’s classic longitudinal data (Newcomb, T.M., 1961), Nakao and Romney measured the “positional stability” of 17 new members in a fraternity during a 15-week period (Nakao, K. and A.K. Romney, 1993). For each week, these individuals were mapped into a two-dimensional MDS diagram based on their relational strength. As individuals may change their positions over time, the lengths of their paths of movement were calculated with a short path indicating a high positional stability.

The positional stability index was used to examine how popular and unpopular individuals differ in the speed with which they found their appropriate social groups. In the area of citation analysis where an author citation network is treated as a social network, centrality type metrics have been used to trace the [dynamics of authors’ influence on a scientific discipline.]

3.1.1 Descriptive methods

The purpose of descriptive analysis is often to detect structural changes in social networks and test how well a sociologic theory is supported by empirical data. With descriptive methods, structural properties of a social network are measured by various metrics and indexes and compared across time to describe the dynamics in nodes, links, or groups in the network. Little research has been found which studied the dynamics at the overall network level.

Node level measures often focus on changes in individuals’ centrality, influence, and other characteristics. To study how an individual’s social position relates to his or her technology adoption behavior, Burkhardt and Brass studied a communication network of 94 employees of an organization at four time points after a new computerized information system was deployed [EN 3]. They found that the centrality (degree and closeness) and power of early adopters of new technology increased over time. Using Newcomb’s classic longitudinal data [EN 28], Nakao and Romney measured the “positional stability” of 17 new members in a fraternity during a 15-week period [EN 27]. For each week, these individuals were mapped into a two-dimensional MDS diagram based on their relational strength. As individuals may change their positions over time, the lengths of their paths of movement were calculated with a short path indicating a high positional stability. The positional stability index was used to examine how popular and unpopular individuals differ in the speed with which they found their appropriate social groups. In the area of citation analysis where an author citation network is treated as a social network, centrality type metrics have been used to trace the dynamics of authors’ influence on a scientific discipline.

[EN 3]. Burkhardt, M.E. & D.J. Brass (1990). Changing patterns or patterns of change: The effects of a change in technology on social network structure and power. Administrative Science Quarterly, 35, 104-127.

[EN 27]. Nakao, K. & A.K. Romney (1993). Longitudinal approach to subgroup formation: Re-analysis of Newcomb's fraternity data. Social Networks, 15, 109-131.

[EN 28]. Newcomb, T.M. (1961). The acquaintance process, ed. Series. New York: Holt, Rinehart, & Winston.

 Anmerkungen Only minor adjustments. Also literature references have been copied. Source is not given. Sichter (Hindemith), WiseWoman

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For example, an author citation network in information science during 1972-1995 was studied in (White, H.D. and K.W. McCain, 1998). A centrality index was calculated based on an author’s mean number of co-citations with other authors. This index was used to reflect the changes in the author’s influence over time.

Link stability has also been researched in various case studies, especially inter-organizational network studies. It is the rate of link breaking and replacement. For example, Ornstein examined the interlocking relations among the 100 largest Canadian companies between 1946-1977 (Ornstein, M.D., 1982). He calculated the percentage of relations that were previously broken but later restored and used in order to test whether the network was dominated by planned liaisons. Similarly, Fennema and Schijf used “chance of restoration” to identify a stable set of interlocking relations among companies across several countries (Fennema, M. and H. Schijf, 1978/79).

Research has focused on group stability and group balance processes to describe group level dynamics. To analyze group balance processes, Doreian and Kapuscinski used Newcomb’s fraternity data (Newcomb, T.M., 1961) to measure relation reciprocity, transitivity, and imbalance across the 15 weeks (Doreian, P., et al.., 1997).

Results for each week were then plotted to study the trend of group balance over time. Group stability is defined in Nakao and Romney’s study as the similarity between the two socio-metrics representing the same group at two different points of time (Nakao, K. and A.K. Romney, 1993). In citation analysis, “Cluster Stability Index” is proposed. It is defined as the number of common elements in two clusters divided by the total number of elements in the two clusters (Small, H.G., 1977). By calculating this index between two similar clusters in two successive time periods, it is

[possible to measure the stability or continuity of a scientific field as represented by a group of authors (Braam, R.R., H.F. Moed, and A.F.J. van Raan, 1991).]

For example, an author citation network in information science during 1972-1995 was studied in [EN 40]. A centrality index is calculated based on an author’s mean number of co-citations with other authors. This index is used to reflect the changes in the author’s influence over time.

[Page 6]

Link stability in terms of link breaking and replacement rate was analyzed in several inter-organizational network studies. Ornstein examined the interlocking relations among the 100 largest Canadian companies between 1946-1977 [EN 30]. He calculated the percentage of relations that were previously broken but later restored and used that to test whether the network was dominated by planned liaisons. Similarly, Fennema and Schijf used “chance of restoration” to identify a stable set of interlocking relations among companies across several countries [EN 16].

To describe group level dynamics, research has focused on group stability and group balance processes. To analyze group balance processes, Doreian and Kapuscinski used Newcomb’s fraternity data [FN 28] to measure relation reciprocity, transitivity, and imbalance across the 15 weeks [FN 14]. Results for each week were then plotted to study the trend of group balance over time. Group stability is defined in Nakao and Romney’s study as the similarity between the two sociomatrices representing the same group at two different points of time [EN 27]. In citation analysis, Small proposes a “Cluster Stability Index”, which is defined as the number of common elements in two clusters divided by the total number of elements in the two clusters [EN 32]. By calculating this index between two similar clusters in two successive time periods, it is possible to quantify the stability or continuity of a scientific field as represented by a group of authors [EN 2].

[EN 2] Braam, R.R., H.F. Moed, & A.F.J. van Raan (1991). Mapping of science by combined co-citation and word analysis ii: Dynamical aspects. Journal of American Society of Information Science, 42(4), 252-266.

[EN 14] Doreian, P., et al. (1997) A brief history of balance through time, in Evolution of social networks, P. Doreian & F.N. Stokman (eds.). Gordon and Breach: Australia. 129-147.

[EN 16] Fennema, M. & H. Schijf (1987/79). Analyzing interlocking directories: Theory and methods. Social Networks, 1, 297-332.

[EN 17] Freeman, L.C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1, 215-240.

[EN 27] Nakao, K. & A.K. Romney (1993) Longitudinal approach to subgroup formation: Re-analysis of Newcomb's fraternity data. Social Networks, 15, 109-131.

[EN 28] Newcomb, T.M. (1961). The acquaintance process, ed. Series. New York: Holt, Rinehart, & Winston.

[EN 30] Ornstein, M.D. (1982). Interlocking directorates in Canada: Evidence from replacement patterns. Social Networks, 4, 3-25.

[EN 32] Small, H.G. (1977). A co-citation model of a scientific specialty: A longitudinal study of collagen research. Social Studies of Science, 7, 139-166.

[EN 40] White, H.D. & K.W. McCain (1998). Visualizing a discipline: An author co-citation analysis of information science, 1972-1995. Journal of American Society of Information Science and Technology, 49(4), 327-355.

 Anmerkungen In the same style as on the previous pages: only minor adjustments, all literature references are copied as well, and the source is not mentioned. Sichter (Hindemith), WiseWoman

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[By calculating this index between two similar clusters in two successive time periods, it is] possible to measure the stability or continuity of a scientific field as represented by a group of authors (Braam, R.R., H.F. Moed, and A.F.J. van Raan, 1991). By calculating this index between two similar clusters in two successive time periods, it is possible to quantify the stability or continuity of a scientific field as represented by a group of authors [2].

[2]. Braam, R.R., H.F. Moed, & A.F.J. van Raan (1991). [...]

 Anmerkungen Continued from previous page Sichter (Hindemith), Bummelchen

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Valdis Krebs [FN 12] worked in analyzing organizations, especially adaptive organizations. He has applied real-world data and model “social network processes”. He has done some work on analyzing the terrorist network surrounding the 9/11 attacks (Krebs, V.; 2001, 2002). He states that one can also apply his analysis to counterterrorist organization and believes it takes a network to fight a network; he therefore prescribes the use of small anti-terror teams.

In his approach, Krebs (2002) takes a snapshot of a network. After many such snapshots, he can see how networks evolve. He argues that one can see patterns at the planning stage of terrorist attacks - a terrorist planning team looks like any other planning team. So, once they get into active planning mode, terrorists’ project map looks like anyone else’s. One can use this knowledge to analyze groups and see where they are in the operational process.

Krebs (2002) also emphasizes that terrorists, like other organizations, do have leaders, so one does not necessarily need all the data – we can obtain a lot of information without it. There has been a great deal of work in link analysis in law enforcement. Link analysis focuses more on objects and people, whereas Krebs’ software concentrates on people and uses social network metrics. Roger Smith (Smith, R., 2001) presented a definition of social cohesion based on network connectivity that leads to an operationalization of social embeddedness. He defined cohesiveness as the minimum number of actors who, if removed from a group, would disconnect the group.

Borgatti Stephen P. (2003) discussed how to identify sets of structurally key players, particularly in the context of attacking [terrorist networks.]

- Mr. Krebs works in analyzing organizations, especially adaptive organizations. He would like to apply what

he does to real-world data and model ‘social network processes’.

- He has done some work on analyzing the terrorist network surrounding the 9/11 attacks. He states that one can also apply his analysis to counterterrorist organization and believes it takes a network to fight a network; he therefore prescribes the use of small anti-terror teams.

- In his approach, Mr. Krebs takes a snapshot of a network. After many such snapshots, he can see how networks evolve. He argues that one can see patterns at the planning stage of terrorist attacks - a terrorist planning team looks like any other planning team. So, once they get into active planning mode, terrorists’ project map looks like anyone else’s. One can use this knowledge to analyze groups and see where they are in the operational process. Mr. Krebs also emphasizes that terrorists, like other organizations, do have leaders, so one does not necessarily need all the data – we can obtain a lot of information without it.

- There has been a lot of work in link analysis in law enforcement. Link analysis focuses more on objects and people, whereas Mr. Krebs’ software concentrates on people and uses social network metrics.

[page 95]

We present a definition of social cohesion based on network connectivity that leads to an operationalization of social embeddedness. We define cohesiveness as the minimum number of actors who, if removed from a group, would disconnect the group.

[page 172]

This paper discusses how to identify sets of structurally key players, particularly in the context of attacking terrorist networks.

 Anmerkungen The given internet link does (and did not) give the same information let alone in the same formulations as the source. Sichter (Hindemith), Bummelchen

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[Borgatti Stephen P. (2003) discussed how to identify sets of structurally key players, particularly in the context of attacking] terrorist networks. Three specific goals are discussed: (a) identifying nodes whose deletion would maximally fragment the network, (b) identifying nodes that, based on structural position alone, are potentially “in the know”, and (c) identifying nodes that are in a position to influence others. Measures of success, in the form of fragmentation and reach indices, are developed and used as cost functions in a combinatorial optimization algorithm. The algorithm is compared against naïve approaches based on choosing the k most central players, as well as against approaches based on group centrality. This paper discusses how to identify sets of structurally key players, particularly in the context of attacking terrorist networks. Three specific goals are discussed: (a) identifying nodes whose deletion would maximally fragment the network, (b) identifying nodes that, based on structural position alone, are potentially “in the know”, and (c) identifying nodes that are in a position to influence others. Measures of success, in the form of fragmentation and reach indices, are developed and used as cost functions in a combinatorial optimization algorithm. The algorithm is compared against naïve approaches based on choosing the k most central players, as well as against approaches based on group centrality.
 Anmerkungen The source is not given. The copied text starts on the previous page. Sichter (Hindemith), Bummelchen

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Berry, Nina et al (2004) presented a multi-disciplinary approach to developing organization software for the study of recruitment and group formation. The need to incorporate aspects of social science added a significant contribution to the vision of the resulting Seldon toolkit. The unique addition of an abstract agent category provided a means for capturing social concepts like cliques, gangs, schools, mosque, etc. in a manner that represents their social conceptualization and not simply as a physical or economical institution. This work provides an overview of the Seldon toolkit and terrorist model developed to study the formation of cliques, which are the primary recruitment entity for terrorist organizations. This is a hybrid architecture providing a unique integration of technology and concepts from interdisciplinary fields of agent-based modeling, social science and simulation. This architecture differs from traditional computational social dynamics simulations because of its multi-level design, abstract agent(s), and interaction based on social networks.

The supporting social terrorist model is based upon the work of Marc Sageman (2004). Marc Sageman, is a former Foreign Service officer who was based in Islamabad from 1987 to 1989, where he worked closely with Afghanistan’s Mujahedin. Sageman work reveals that the mujahedin have no documented history of psychological or social pathologies; they are considered healthy [members of their society.]

[Abstract ]

The Seldon project represents a multi-disciplinary approach to developing organization software for the study of recruitment and group formation. The need to incorporate aspects of social science added a significant contribution to the vision of the resulting Seldon toolkit. The unique addition of an abstract agent category provided a means for capturing social concepts like cliques, gangs, schools, mosque, etc. in a manner that represents their social conceptualization and not simply as a physical or economical institution. This paper provides an overview of the Seldon toolkit and terrorist model developed to study the formation of cliques, which are the primary recruitment entity for terrorist organizations. [...]

[right column]

The general Seldon toolkit is a hybrid architecture providing a unique integration of technology and concepts from the interdisciplinary fields of agent-based modeling, social science, and simulation. This architecture differs from traditional computational social dynamic simulations because of its multi-level design, abstract agent(s), and interactions based on social networks. [...]

[page 2, left column]

The supporting social terrorist model is based upon the work of Marc Sageman (Sageman 2004). Marc Sageman, Ph.D., M.D., is a former Foreign Service officer who was based in Islamabad from 1987 to 1989, where he worked closely with Afghanistan's mujahedin. [...]

Sageman work reveals that the mujahedin have no documented history of psychological or social pathologies: they are considered healthy members of their society.

 Anmerkungen The description of the work of Berry et al. is actually taken from parts of their paper. Could also be classified as a pawn sacrifice, a "Bauernopfer". The incorrect grammar from Berry in the last sentence ("Sageman work" instead of "Sageman's work") is incorrect both in Berry et al and in Nm. Sichter (Hindemith), WiseWoman

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He further states that they have formed into social networks or “clusters” based on some common experience. Their commonality is based on their expatriate status which results in a level of isolation. Their status in the host country and their shared assumed sense of isolation result in the development of clusters. Berry N. et al integrated agent based and social modeling approach in the Seldon project. Second, they have formed into social networks or ‘clusters’ based on some common experience. Their commonality is based on their expatriate status which results in a level of isolation. Their status in their host country and their shared assumed sense of isolation result in the development of clusters. [...]

This analysis of the basis for participation supports the integrated agent-based and social network modeling approach we have taken with Seldon.

 Anmerkungen The source is only mentioned in the last sentence, the extent of the text taken is not made clear. Sichter (Hindemith), WiseWoman

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In exploring the distribution of the severity of events in global terrorism, Clauset Aaron and Maxwell Young (2005) found a surprising and robust feature: scale invariance. Traditional analyses of terrorism have typically viewed catastrophic events such as the 1995 truck bombing of the American embassy in Nairobi, Kenya, which killed or injured more than 5200. However, the property of scale invariance suggests that these are instead a part of a statistically significant global pattern in terrorism. Further, they showed that there is little reason to believe that the appearance of power laws in the distribution of the severity of an event is the result of either reporting bias or changes in database management.

There are many generative mechanisms in the literature for power laws, although many of them are unappealing for explaining the structure (Maxwell Young, 2005) found in global terrorism. The highly abstract model of competition between non-state actors and states, which they proposed, analyzed and extended via the mixtures model, is likely to be too simple to capture the fine structure of global terrorism. However, their model and the statistically significant empirical regularities which showed by the author will frame future efforts to understand global terrorism.

In exploring the distribution of the severity of events in global terrorism, we have found a surprising and robust feature: scale invariance. Traditional analyses of terrorism have typically viewed catastrophic events such as the 1995 truck bombing of the American embassy in Nairobi, Kenya, which killed or injured more than 5 200, as outliers. However, the property of scale invariance suggests that these are instead a part of a statistically significant global pattern in terrorism. Further, we find little reason to believe that the appearance of power laws in the distribution of the severity of an event is the result of either reporting bias or changes in database management. [...]

[2nd column, 31ff]

There are many generative mechanisms in the literature for power laws, although many of them are unappealing for explaining the structure we find in global terrorism. The highly abstract model of competition between non-state actors and states, which we propose, analyze and extend via the mixtures model, is likely too simple to capture the fine structure of global terrorism. However, we hope that our model and the statistically significant empirical regularities which we show here will frame future efforts to understand global terrorism.

 Anmerkungen In describing the findings of Clauset & Young (2005), Nm actually copies quite literally from their paper, which is not mentioned in the bibliography. Note that reference is also made to "Maxwell Young, 2005", which, however, is probably meant to be the same paper. The name of the first author is Aaron Clauset, not Clauset Aaron. One could also classify this as a pawn sacrifice ("Bauernopfer"), since a partial reference is given, although the extent of the text taken is not make clear. Sichter (Hindemith), WiseWoman

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2.13.2 Statistical Methods

Statistical analysis examines social network dynamics quantitatively. It aims not only to identify and examine the network changes, but also to account for the causes which brought these changes. Structural changes are assumed to result from some [stochastic processes of network effects such as reciprocity, transitivity, and balance (Snijders, T.A.B., 2001).]

3.1.2 Statistical Methods

Statistical analysis of social network dynamics aims not only at detecting and describing network changes but also at explaining why these changes occur. With statistical methods, structural changes are assumed to result from some stochastic processes of network effects such as reciprocity, transitivity, and balance [EN 34].

[EN 34]. Snijders, T.A.B. (2001). The statistical evaluation of social network dynamics. Sociological Methodology, 31, 361-395.

 Anmerkungen No source given. Text continues on the next page: Nm/Fragment_086_01 Sichter (Hindemith), WiseWoman

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[Structural changes are assumed to result from some] stochastic processes of network effects such as reciprocity, transitivity, and balance (Snijders, T.A.B., 2001). In statistical analysis, links are modeled as random variables that can be in different states at different times. The purpose is to identify which network models fits best with empirical data and observed structural changes.

Discrete or continuous Markov models are often used in statistical analysis. The Markov models are based on the assumptions that a particular state of a process is dependent current state but not on any previous state (Leenders, R., 1997; Snijders, T.A.B., 1997; Snijders, T.A.B., 2001). The analysis is carried out with the help of transition and intensity matrix. Transition matrix contains the conditional probabilities one state to previous state, while the intensity matrix contains the transition rates (Hallinan, M.T.,1978/79); Leenders, R., 1997).

With statistical methods, structural changes are assumed to result from some stochastic processes of network effects such as reciprocity, transitivity, and balance [EN 34]. In this type of analysis, links are modeled as random variables that can be in different states [...] at different time. The

[P. 7]

purpose is to examine which network effect fits the empirical data and better accounts for the observed structural changes.

Discrete or continuous Markov models are often used in statistical analysis. The most important property of a Markov model is that the future state of a process is dependent only on the current state but not on any previous state [EN 25, EN 33, EN 34]. The process is governed by a transition matrix, which contains the conditional probabilities of changing from the initial state to the current state, and the intensity matrix whose elements are transition rates [EN 19, EN 25].

[EN 19] Hallinan, M.T. (1978/79). The process of friendship formation. Social Networks, 1, 193-210.

[EN 25] Leenders, R. (1997). Evolution of friendship and best friendship choices, in Evolution of social networks, P. Doreian & F.N. Stokman (eds.). Gordon and Breach: Australia.

[EN 33] Snijders, T.A.B. (1997). Stochastic actor-oriented models for network change, in Evolution of social networks, P. Doreian & F.N. Stokman (eds.). Gordon and Breach: Australia.

[EN 34] Snijders, T.A.B. (2001). The statistical evaluation of social network dynamics. Sociological Methodology, 31, 361-395.

 Anmerkungen Copied text starts on the previous page (Nm/Fragment_085_28). No source is given. Sichter (Hindemith), WiseWoman

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2.13.3 Simulation Methods

The simulation methods exploit multi agent technology to analyse the network dynamics, in contrary to descriptive or statistical methods which examine social network dynamics quantitatively. In the simulation method, members in a social network are often modeled intelligent agents with ability to behave and make decisions based on certain criteria in a particular situation. The collective behaviors of all members in a network will determine [how the network evolves from one structure to another in a considered scenario.]

3.1.3 Simulation Methods

Unlike descriptive or statistical methods, which examine social network dynamics quantitatively, simulation methods rely on multi-agent technology to analyze network dynamics. In this method, members in a social network are often modeled and implemented as computer agents who have the abilities to behave and make decisions based on certain criteria. The collective behaviors of all members in a network will determine how the network evolves from one structure to another.

 Anmerkungen No source given. Sichter (Hindemith), WiseWoman

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[The collective behaviors of all members in a network will determine] how the network evolves from one structure to another in a considered scenario.

There are many examples of employing simulation methods in social network analysis. For instance, Ornstein employs agent-based simulation to identify how social choices of establishing or ceasing a relationship with others affect the overall structure of a network (Hummon, N.P., 2000). The basic assumption is that every relationship has an associated costs and benefits. Individuals aim to maximize their utilities by altering their relationships with others and social network will keep on evolving until joint utility of all members is maximized.

The collective behaviors of all members in a network will determine how the network evolves from one structure to another.

Several SNA studies have employed simulation methods. For example, Ornstein uses agent-based simulation to study how individuals’ social choices of establishing or ceasing a

[P. 8]

relationship with others affect the structure of a network [EN 20]. The basic assumption is that maintaining a relationship has its associated costs and benefits and individuals aim to maximize their utilities by altering their relationships with others. A social network will keep changing until the joint utility of all members is maximized.

[EN 20] Hummon, N.P. (2000). Utility and dynamic social networks. Social Networks, 22, 221-249.

 Anmerkungen No source given Sichter (Hindemith), WiseWoman

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Professor Carley and her colleagues are working on a number of projects related to counterterrorism. All their models contain AI, complexity approaches, and are multi-agent.
• BIOWAR –Carley, K., M.; Douglas B. Fridsma; Alex Yahja (2002) described “BIOWAR”; a simulation system that uses cognitively realistic agents embedded in social, knowledge and work networks. The idea is to describe how people participating in these networks acquire disease, manifest symptoms, seek information and treatment, and recover from illness. The system uses a model of diseases and symptoms to analyse the agents who come in contact with infectious agents through their social and work networks become ill. The illnesses alter their behavior, changing both the propagation of the disease, and the manifestation of the disease on the population. A number of simulations were completed by them that were targeted to examine the effect of contagious and non-contagious illnesses in high-alert (agents have knowledge of a potential disease outbreak) or low alert states. Agents who believe they may be ill and have knowledge of a potential outbreak are more likely to seek care than those who do not.
Professor Carley described six ongoing projects related to counterterrorism being conducted by her research group. All their models contain AI, complexity approaches, and are multi-agent.

[page 105]

We describe a simulation system called BIOWAR which uses cognitively realistic agents embedded in social, knowledge and work networks to describe how people interacting in these networks acquire disease, manifest symptoms, seek information and treatment, and recover from illness. Using a model of diseases and symptoms, agents who come in contact with infectious agents through their social and work networks become ill. These illnesses alter their behavior, changing both the propagation of the disease, and the manifestation of the disease on the population.

Presently, we have completed a number of simulations which examine the effect of contagious and non-contagious illnesses in high-alert (agents have knowledge of a potential disease outbreak) or low alert states. Agents who believe they may be ill and have knowledge of a potential outbreak are more likely to seek care than those who do not.

 Anmerkungen No source given Sichter (Hindemith), Bummelchen

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[In this] system authors compared results of low alert states to known influenza epidemics and to data containing emergency room visits, pharmacy purchases and absenteeism. Although the peak incidence of the simulated outbreak is larger than the peak incidence seen in the population data, the simulation results are temporally similar to those seen in the population data. They hoped that this simulation framework will allow them to ask ‘what-if’ questions regarding appropriate response and detection strategies for both natural and man-made epidemics. This is a city scale multi-agent model of weaponized bioterrorist attacks for intelligence and training. At present the model is running with 100,000 agents (this number will be increased). All agents have real social networks and the model contains real city data -hospitals, schools etc. Agents are as realistic as possible and contain a cognitive model.
• DYNET—Dynamic Networks. The team is building a model of how networks adapt, evolve and change in response to various types of attacks e.g. infowar or assassination.
[page 105]

We have compared results of low alert states to known influenza epidemics and to data containing emergency room visits, pharmacy purchases and absenteeism. Although the peak incidence of the simulated outbreak is larger than the peak incidence seen in the population data, the simulation results are temporally similar to those seen in the population data. [...]. It is hoped that this simulation framework will allow us to ask ‘what-if’ questions regarding appropriate response and detection strategies for both natural and man-made epidemics.

[page 18]

this is a cityscale multi-agent model of weaponized bioterrorist attacks for intelligence and training. At present the model is running with 100,000 agents (this number will be increased). All agents have real social networks and the model contains real city data - hospitals, schools etc. Agents are as realistic as possible and contain a cognitive model.

[...]

DYNET – Dynamic Networks. The team is building a model of how networks adapt, evolve and change in response to various types of attacks e.g. infowar or assassination

 Anmerkungen No source given Sichter (Hindemith), Bummelchen

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NETEST – It is based on the combination of multi-agent technology with hierarchical Bayesian inference models and biased net models to produce accurate posterior network representations. Bayesian inference models produce representations of a network’s structure and informant accuracy by combining prior network and accuracy data with informant perceptions of a network. Biased net theory examines and captures the biases that may be present within a specific network or group of networks. NETEST provides functionalities to estimate a network’s size, determine its membership and structure, determine areas of the network where data is missing, perform cost and benefit analysis of additional information, assess group level capabilities [embedded in the network, and pose “what if” scenarios to destabilize a network and predict its evolution over time.] NETEST is a tool that combines multiagent technology with hierarchical Bayesian inference models and biased net models to produce accurate posterior representations of a network. Bayesian inference models produce representations of a network’s structure and informant accuracy by combining prior network and accuracy data with informant perceptions of a network. Biased net theory examines and captures the biases that may exist in a specific network or set of networks. Using NETEST, an investigator has the power to estimate a network’s size, determine its membership and structure, determine areas of the network where data is missing, perform cost/benefit analysis of additional information, assess group level capabilities embedded in the network, and pose “what if” scenarios to destabilize a network and predict its evolution over time.
 Anmerkungen The source is given in the bibliography, but nowhere close to this text fragment. Sichter (Hindemith), WiseWoman

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[NETEST provides functionalities to estimate a network’s size, determine its membership and structure, determine areas of the network where data is missing, perform cost and benefit analysis of additional information, assess group level capabilities] embedded in the network, and pose “what if” scenarios to destabilize a network and predict its evolution over time. Using NETEST, an investigator has the power to estimate a network’s size, determine its membership and structure, determine areas of the network where data is missing, perform cost/benefit analysis of additional information, assess group level capabilities embedded in the network, and pose “what if” scenarios to destabilize a network and predict its evolution over time.
 Anmerkungen The copied text starts on the previous page. A reference is not given. Sichter (Hindemith), WiseWoman

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THREATFINDER – this tool originated in the corporate

realm. Certain individuals within an organization are more likely than others to pose threats. The research involves looking for threat indicators for who could launch an attack i.e. using a network/organizational perspective, Threatfinder identifies threats within an organization.

Tsvetovat Maksim and Kathleen Carley (2002) proposed a methodology for realistically simulating terrorist networks in order to develop network metrics to test strategies of destabilizing them, provided a model of antiterrorist policy. They have not discussed behavior or real group interaction and the model is limited in scope and depth. It may be used as a starting point to focus terrorist organization and network

Michael J. North and his colleagues (North, J. M; Nicholson, T. C; and Jerry R. V., 2006) worked on a model of terrorist networks— looking at relationships between different terrorist structures. Their models show whether or not particular terrorist group fits in a given structure.

The authors modeled terrorists as genetic algorithms. The models also look at counter forces. Social factors, such as propagation of dangerous ideas, are built into the model. The model uses REPAST tool (developed by University of Chicago) which, as opposed to SWARM (which they believe is getting old).

[page 18]

THREATFINDER – this tool originated in the corporate realm. Certain individuals within an organization are more likely than others to pose threats. The research involves looking for threat indicators for who could launch an attack i.e. using a network/organizational perspective, Threatfinder identifies threats within an organization.

[page 20]

The paper proposes a methodology for realistically simulating terrorist networks in order to develop network metrics to test strategies of destabilizing them; provides a model of anti-terrorist policy; doesn't provide behavior or real group interaction and is limited in scope and depth; may be used as a starting point; focus: terrorist organization and network

[page 14]

- He is working on a model of terrorist networks – looking at relationships between different terrorist structures.

- His models let people test whether or not a particular terrorist group fits a given structure.

- Terrorists are modeled as genetic algorithms.

- The model also looks at counterforces.

- Social factors, such as the propagation of dangerous ideas, are built into the model.

- The model uses REPAST (developed by the University of Chicago) which is written in Java, as opposed to SWARM which is written in C (and he believes is getting old).

 Anmerkungen No source given Sichter (Hindemith), Bummelchen

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Some SNA research, especially citation analysis, has employed visualization techniques to study network dynamics. This approach relies on visual presentations of social networks and is quite different from descriptive, statistical, and simulation methods.

2.14 SUMMARY

The research in the dynamic of SNA studies provides a good foundation for criminal network dynamics analysis. Although the purpose of analyzing criminal network dynamics is not to test theories, the methods, measures, and models from SNA can help detect and describe structural changes, extract the patterns of these changes, and even predict the future activities and structure of criminal organizations.

Some SNA research, especially citation analysis, has employed visualization techniques to study network dynamics. This approach relies on visual presentations of social networks and is quite different from the descriptive, statistical, and simulation methods. [...]

[Page 10]

Research in these dynamic SNA studies provides a good foundation for criminal network dynamics analysis. Although the purpose of analyzing criminal network dynamics is not to test theories, the methods, measures, and models from SNA can help detect and describe structural changes, extract the patterns of these changes, and even predict the future activities and structure of criminal organizations.

 Anmerkungen Word-by-word copy from the source. The source is not referenced. Sichter (Hindemith), WiseWoman

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Moreover, as described earlier, data collection is difficult for any network analysis because it is hard to create a complete network. It is difficult especially to gain information on terrorist networks. Terrorist organizations do not provide information on their membership structure, and the intelligence agencies rarely allow researchers to use their intelligence data. A number of academic researchers focus primarily on data collection on terrorist

organizations, analyzing the information through descriptive and straight forward methods (for example, Krebs V., 2002, Sageman, M., 2004) as described earlier.

Data collection is difficult for any network analysis because it is hard to create a complete network. It is especially difficult to gain information on terrorist networks. Terrorist organizations do not provide information on their members, and the government rarely allows researchers to use their intelligence data. A number of academic researchers focus primarily on data collection on terrorist organizations, analyzing the information through description and straightforward modeling.
 Anmerkungen Unbelievable: For the third time (see Nm/Fragment_033_15 and Nm/Fragment_040_22) we are confronted with the same six lines from Ressler (2006) with only slight modifications. Still no mention of the source. Sichter (Graf Isolan), Hindemith

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One promising activity is the development of a major terrorism web portal at the University of Arizona’s Artificial Intelligence Lab. The website makes social network tools and data related to terrorism publicly available (Reid, E., et al., 2004). One example is the Terrorism Knowledge Portal, a database consisting of over 360,000 terrorism news articles and related Web pages coming from various high-quality terrorism websites, major search engines,

and new portals.

[p. 6]

One promising activity is the development of a major terrorism web portal at the University of Arizona’s Artificial Intelligence Center. This website makes social network tools and data related to terrorism publicly available. [EN 18] One example is the Terrorism

[p.7]

Knowledge Portal, a database consisting of over 360,000 terrorism news articles and related Web pages coming from various high-quality terrorism Web sites, major search engines, and news portals.

[EN 18] Edna Reid, Jialun Quin, Wingyan Chung, Jennifer Xu, Yilu Zhou, Rob Schumaker, Marc Sageman, and Hsinchun Chen, “Terrorism Knowledge Discovery Project: A Knowledge Discovery Approach to Address the Threats of Terrorism,” (Working paper, 2004).

 Anmerkungen Nothing marked as a citation, no source named. Sichter (Graf Isolan), Hindemith

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Terrorists seldom operate in a vacuum but interact with one another to carry out terrorist activities. To perform terrorist activities requires collaboration among terrorists. Relationships between individual terrorists for the basis of terrorism are essential for the smooth operation of a terrorist organization, which can be viewed as a network consisting of nodes (for example terrorists, terrorist camps, supporting countries, etc.) and links (for example, communicates with, or trained at, etc.). In terrorist networks, groups or cells, within which members have close relationships, may be present. One group may also interact with other groups. For example, some key nodes (key players) may act as leaders to control activities of a group. Some others may serve as gatekeepers to ensure smooth flow of information or illicit goods. Criminals seldom operate in a vacuum but interact with one another to carry out various illegal activities. In particular, organized crimes such as terrorism, [...] require collaboration among offenders. Relationships between individual offenders form the basis for organized crimes [18] and are essential for smooth operation of a criminal enterprise, which can be viewed as a network consisting of nodes (individual offenders) and links (relationships). In criminal networks, there may exist groups or teams, within which members

have close relationships. One group also may interact with other groups [...]. For example, some key members may act as leaders to control activities of a group. Some others may serve as gatekeepers to ensure smooth flow of information or illicit goods.

 Anmerkungen "Criminals" become "Terrorists", and a few more adaptations. The original source is not referenced. Note that the very same paragraph can also be found at the beginning of the thesis: Nm/Fragment_019_01 Sichter (Hindemith), Bummelchen

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In social network literature, researchers have examined a broad range of types of ties (Menzel, H. and Katz, E., 1957). These include communication ties (such as who talks to whom or who gives information or advice to whom), formal ties (such as who reports to whom), affective ties (such as who likes whom, or who trust whom), material or work flow ties (such as who gives bomb making material or other resources to whom), proximity ties (who is spatially or electronically close to whom). Networks are typically multiplex, that is, actors share more than one type of tie. Network researchers have examined a broad range of types of ties. These include communication ties (such as who talks to whom, or who gives information or advice to whom), formal ties (such as who reports to whom), affective ties (such as who likes whom, or who trusts whom), material or work flow ties (such as who gives money or other resources to whom), proximity ties (who is spatially or electronically close to whom), and cognitive ties (such as who knows who knows whom). Networks are typically mutiplex [sic], that is, actors share more than one type of tie.
 Anmerkungen The source of this section is found in the list of references but not here. Moreover nothing is marked as a citation. Note: Page 19 and page 93 of the thesis are nearly identical, so Katz et al. (2004) has also been copied on page 19: Nm/Fragment_019_16 Sichter (Graf Isolan), Bummelchen

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For example, two terrorists might have a formal tie (one is a footsoldier or a newly recruited person in a terrorist cell and reports to another, who is a cell leader) and an affective tie (they are friends); and may also have a proximity tie (i.e., they reside in the same building and their apartments are two doors away on the same floor).

Network researchers have distinguished between strong ties (such as family and friends) and weak ties such as acquaintances (Granovetter, M., 1973, 1982). This distinction will involve a multitude of facets, including affect, mutual obligations, reciprocity, and intensity. Strong ties are particularly valuable when an individual seeks socio-emotional support and often entail a high level of trust. Weak ties are more valuable when individuals are seeking diverse or unique information from someone outside their regular frequent contacts.

Ties may be non-directional (for example, Atta attends meeting with Nawaf Alhazmi) or vary in direction (for instance, Bin Laden gives advice to Atta vs. Atta gets advice from Bin Laden). They may vary in content (Atta talks with Khalid about the trust of his friends in using them as human bombs) and Khalid about his recent meeting with Bin Laden), frequency (daily, weekly, monthly, etc.), and medium (face-to-face conversation, written memos, email, fax, instant messages, etc.). Finally ties may vary in sign, ranging from positive (Iraqis like Zarqawi) to negative (Jordanians dislike Zarqawi).

[p. 308]

For example, two academic colleagues might have a formal tie (one is an assistant professor and reports to the other. who is the department chairperson)

[p.309]

and an affective tie (they are friends) and a proximity tie (their offices are two doors away).

Network researchers have distinguished between strong ties (such as family and friends) and weak ties (such as acquaintances) (Granovetter, 1973. 1982). This distinction can involve a multitude of facets, including affect, mutual obligations, reciprocity, and intensity. Strong ties are particularly valuable when an individual seeks socioemotional support and often entail a high level of trust. Weak ties are more valuable when individuals are seeking diverse or unique information from someone outside their regular frequent contacts. [...]

Ties may be nondirectional (Joe attends a meeting with Jane) or vary in direction (Joe gives advice to Jane vs. Joe gets advice from Jane). They may also vary in content (Joe talks to Jack about the weather and to Jane about sports), frequency (daily. weekly, monthly, etc.), and medium (face-to-face conversation, written memos, e-mail, instant messaging, etc.). Finally, ties may vary in sign, ranging from positive (Joe likes Jane) to negative (Joe dislikes Jane).

 Anmerkungen The same text, only the examples have been adapted to the subject at hand, terrorism. No reference given. Sichter (Graf Isolan), Bummelchen

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Structural network patterns in terms of subgroups and individual roles are important in understanding the organization and operation of terrorist networks. Such knowledge can help law enforcement and intelligence agencies to disrupt terrorist networks and develop effective control [strategies to combat terrorism.] Structural network patterns in terms of subgroups, between-group interactions, and individual roles thus are important to understanding the organization, structure, and operation of criminal enterprises. Such knowledge can help law enforcement and intelligence agencies disrupt criminal networks and develop effective control strategies to combat organized crimes such as narcotic trafficking and terrorism.
 Anmerkungen nothing marked as a citation, no reference given Sichter (Graf Isolan), Bummelchen

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For example, capture of central members in a network may effectively upset the operational network and put a terrorist organization out of action (Baker, W.E. and Faulkner, R.R., 1993; McAndrew, D., 1999; Sparrow, M., 1991). Subgroups and interaction patterns between groups are helpful in finding a network’s overall structure, which often reveals points of vulnerability (Evan, W. M., 1972; Ronfeldt, D., Arquilla, J., 2001 ). [For exam]ple, removal of central members in a network may effectively upset the operational network and put a criminal enterprise out of action [3, 17, 21]. Subgroups and interaction patterns between groups are helpful for finding a network’s overall structure, which often reveals points of vulnerability [9, 19].

[EN 3] Baker, W. E., Faulkner R. R.: The social organization of conspiracy: illegal networks in the heavy electrical equipment industry. American Sociological Review, Vol. 58, No. 12. (1993) 837–860.

[EN 17] McAndrew, D.: The structural analysis of criminal networks. In: Canter, D., Alison, L. (eds.): The Social Psychology of Crime: Groups, Teams, and Networks, Offender Profiling Series, III, Aldershot, Dartmouth (1999) 53–94.

[EN 21] Sparrow, M. K.: The application of network analysis to criminal intelligence: An assessment of the prospects. Social Networks, Vol. 13. (1991) 251–274.

[EN 9] Evan, W. M.: An organization-set model of interorganizational relations. In: M. Tuite, R. Chisholm, M. Radnor (eds.): Interorganizational Decision-making. Aldine, Chicago (1972) 181–200.

[EN 19] Ronfeldt, D., Arquilla, J.: What next for networks and netwars? In: Arquilla, J., Ronfeldt, D. (eds.): Networks and Netwars: The Future of Terror, Crime, and Militancy. Rand Press, (2001).

 Anmerkungen Continuation from previous page. This section appears in Nm's thesis for the second time, the first time was here: Nm/Fragment 021 01 Sichter (Graf Isolan), Bummelchen

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Graph Theory [FN 14]

The fundamental concept of graph theory is a graph, which (despite the name) is best thought of as a mathematical object rather than a diagram, even though graphs have a very natural graphical representation.

[FN 14] Most of the concepts discussed in this section are taken from West B. Douglas (2001).

Graphs

The fundamental concept of graph theory is the graph, which (despite the name) is best thought of as a mathematical object rather than a diagram, even though graphs have a very natural graphical representation.

 Anmerkungen The source is not given. West B. Douglas is listed in the bibliography under "West", but the author is named Douglas B. West and the book was published in 1996 (2nd edition was 2001): http://www.math.uiuc.edu/~west/igt/ Sichter (Hindemith), WiseWoman

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When looking at visualizations of graphs such as Figure 3.1, it is important to realize that the only information contained in a diagram is adjacency; the position of nodes in a plane (and therefore the length of lines) is arbitrary unless otherwise specified. Hence it is usually dangerous to draw conclusions based on the spatial position of the nodes. For example, it is tempting to conclude that nodes in the middle of a diagram are more important than nodes on the peripheries, but this will often – if not usually – be a mistake.

When using graphs to represent terrorist networks, we typically use each line to represent instances of the same social relation, so that if (a, b) indicates a friendship between a person located at node a, and a person located at node b, then (d, e) indicates a friendship between d and e. Thus, each distinct social relation that is empirically measured on the same group of people is represented by separate graphs, which are likely to have different structures (after all, who talks to whom, is not the same as who dislikes whom).

The natural graphical representation of an adjacency matrix is a [table, such as shown in Figure 3. 2.]

When looking at visualizations of graphs such as Figure 1, it is important to realize that the only information contained in the diagram is adjacency; the position of nodes in the plane (and therefore the length of lines) is arbitrary unless otherwise specified. Hence it is usually dangerous to draw conclusions based on the spatial position of the nodes. For example, it is tempting to conclude that nodes in the middle of a diagram are more important than nodes on the peripheries, but this will often – if not usually – be a mistake.

When used to represent social networks, we typically use each line to represent instances of the same social relation, so that if (a, b) indicates a friendship between the person located at node a and the person located at node b, then (d, e) indicates a friendship between d and e. Thus, each distinct social relation that is empirically measured on the same group of people is represented by separate graphs, which are likely to have different structures (after all, who talks to whom is not the same as who dislikes whom).

[...] The natural graphical representation of an adjacency matrix is a table, such as shown in Figure 2.

 Anmerkungen The source is not given. Note the slight adaptation to make the text fit the terrorist topic of the thesis. Sichter (Hindemith), WiseWoman

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[The natural graphical representation of an adjacency matrix is a] table, such as shown in Figure 3. 2.

[TABLE, same as in source but extended by one row and one column]

Figure 3.2. Adjacency matrix for graph in Figure 3.1.

Examining either Figure 3.1 or Figure 3.2, we can see that not every vertex is adjacent to every other. A graph in which all vertices are adjacent to all others is said to be complete. The extent to which a graph is complete is indicated by its density, which is defined as the number of edges divided by the number possible. If self-loops are excluded, then the number possible is n(n-1)/2. Hence the density of the graph in Figure 3.1 is 7/21 = 0.33.

The natural graphical representation of an adjacency matrix is a table, such as

shown in Figure 2.

[TABLE]

Figure 2. Adjacency matrix for graph in Figure 1.

Examining either Figure 1 or Figure 2, we can see that not every vertex is adjacent to every other. A graph in which all vertices are adjacent to all others is said to be complete. The extent to which a graph is complete is indicated by its density, which is defined as the number of edges divided by the number possible. If self-loops are excluded, then the number possible is n(n-1)/2. [...] Hence the density of the graph in Figure 1 is 6/15 = 0.40.

 Anmerkungen The source is not given anywhere in the thesis. Sichter (Hindemith), Bummelchen

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Graphs can be undirected or directed. The adjacency matrix of an undirected graph (as shown in Figure 3.2) is symmetric. An undirected edge joining vertices $u, v \in V$ is denoted by $\{u, v\}$.

In directed graphs, each directed edge (arc) has an origin (tail) and a destination (head). An edge with origin $u \in V$ is represented by an order pair $(u, v)$. As a shorthand notation, an edge $\{u, v\}$ can also be denoted by $uv$. It is to note that, in a directed graph, $uv$ is short for $(u, v)$, while in an undirected graph, $uv$ and $vu$ are the same and both stands for $\{u, v\}$. Graphs that can have directed as well undirected edges are called mixed graphs, but such graphs are encountered rarely.

Graphs can be undirected or directed. In undirected graphs, the order of the endvertices of an edge is immaterial. An undirected edge joining vertices $u, v \in V$ is denoted by $\{u, v\}$. In directed graphs, each directed edge (arc) has an origin (tail) and a destination (head). An edge with origin $u \in V$ and destination $v \in V$ is represented by an ordered pair $(u, v)$. As a shorthand notation, an edge $\{u, v\}$ or $(u, v)$ can also be denoted by $uv$. In a directed graph, $uv$ is short for $(u, v)$, while in an undirected graph, $uv$ and $vu$ are the same and both stand for $\{u, v\}$. [...]. Graphs that can have directed edges as well as undirected edges are called mixed graphs, but such graphs are encountered rarely [...]
 Anmerkungen The source is not mentioned anywhere in the thesis. The definitions given here are certainly standard and don't need to be quoted. However, Nm uses for several passages the same wording as the source. Note also that "An edge with origin $u \in V$ is represented by an order pair $(u, v)$" is a curious abbreviation of the statement "An edge with origin $u \in V$ and destination $v \in V$ is represented by an ordered pair $(u, v)$" in the source. Sichter (Hindemith). WiseWoman

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Similarly, any pair of vertices in which one vertex can reach the other via a sequence of adjacent vertices is called reachable. If we determine reachability for every pair of vertices, we can construct a reachability matrix R such as depicted in Figure 3.3. The matrix R can be thought of as the result of applying transitive closure to the adjacency matrix A.

[FIGURE, different from source]

Figure 3.3. Reachability matrix

A component of a graph is defined as a maximal subgraph in which a path exists from every node to every other (i.e., they are mutually reachable). The size of a component is defined as the number of nodes it contains. A connected graph has only one component.

A sequence of adjacent vertices $v_0, v_1, \ldots, v_n$ is known as a walk. A walk can also be seen as a sequence of incident edges, where two edges are said to be incident if they share exactly one vertex. A walk in which no vertex occurs more than once is known as a path.

Similarly, any pair of vertices in which one vertex can reach the other via a sequence of adjacent vertices is called reachable. If we determine reachability for every pair of vertices, we can construct a reachability matrix R such as depicted in Figure 3. The matrix R can be thought of as the result of applying transitive closure to the adjacency matrix A.

[FIGURE]

Figure 3

A component of a graph is defined as a maximal subgraph in which a path exists from every node to every other (i.e., they are mutually reachable). The size of a component is defined as the number of nodes it contains. A connected graph has only one component.

A sequence of adjacent vertices $v_0, v_1, \ldots, v_n$ is known as a walk. [...]. A walk can also be seen as a sequence of incident edges, where two edges are said to be incident if they share exactly one vertex. A walk in which no vertex occurs more than once is known as a path.

 Anmerkungen No source given. The table Nm gives (figure 3.3.) can be found in the source on page 4 (figure 4). Sichter (Hindemith), WiseWoman

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A walk in which no edge occurs more than once is known as a trail. In Figure 3.1, the sequence a, b, c, e, d, c, g is a trail but not a path. Every path is a trail, and every trail is a walk. A walk is closed if $v_o = v_n$. A cycle can be defined as a closed path in which n >= 3. The sequence c, e, d in Figure 3.1 is a cycle. A tree is a connected graph that contains no cycles. In a tree, every pair of points is connected by a unique path. That is, a tree is a graph in which any two vertices are connected by exactly one path.

The length of a walk (and therefore a path or trail) is defined as the number of edges it contains. For example, in Figure 3.1, the path a, b, c, d, e has length 4. A walk between two vertices whose length is as short as any other walk connecting the same pair of vertices is called a geodesic. Of course, all geodesics are paths. Geodesics are not necessarily unique. From vertex a to vertex f in Figure 3.1, there are two geodesics: a, b, c, d, e, f and a, b, c, g, e, f.

The graph-theoretic distance (usually shortened to just “distance”) between two vertices is defined as the length of a geodesic that connects them. If we compute the distance between every pair of vertices, we can construct a distance matrix D such as depicted in Figure 3.3. The maximum distance in a graph defines the graph’s diameter. As shown in Figure 3.3, the diameter of the graph in Figure 3.1 is 4. If the graph is not connected, then there exist pairs of vertices that are not mutually reachable so that the distance between them is not defined and the diameter of such a graph is also not defined.

A walk in which no edge occurs more than once is known as a trail. In Figure 3, the sequence a, b, c, e, d, c, g is a trail but not a path. Every path is a trail, and every trail is a walk. A walk is closed if $v_o = v_n$. A cycle can be defined as a closed path in which n >= 3. The sequence c, e, d in Figure 3 is a cycle. A tree is a connected graph that contains no cycles. In a tree, every pair of points is connected by a unique path. That is, there is only one way to get from A to B.

The length of a walk (and therefore a path or trail) is defined as the number of edges it contains. For example, in Figure 3, the path a, b, c, d, e has length 4. A walk between twovertices whose length is as short as any other walk connecting the same pair of vertices iscalled a geodesic. Of course, all geodesics are paths. Geodesics are not necessarily unique. From vertex a to vertex f in Figure 1, there are two geodesics: a, b, c, d, e, f and a, b, c, g, e, f.

The graph-theoretic distance (usually shortened to just “distance”) between two vertices is defined as the length of a geodesic that connects them. If we compute the distance between every pair of vertices, we can construct a distance matrix D such as depicted in Figure 4. The maximum distance in a graph defines the graph’s diameter. As shown in [page 4] Figure 4, the diameter of the graph in Figure 1 is 4. If the graph is not connected, then there exist pairs of vertices that are not mutually reachable so that the distance between them is not defined and the diameter of such a graph is also not defined.

 Anmerkungen No source given, very minor adjustments Sichter (Hindemith), WiseWoman

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In both undirected and directed graphs', we may allow the edge set E to contain the same edge several times, that is, E can be a multiset. If an edge occurs several times in E, the copies of that edge are called parallel edges. Graphs with parallel edges are also called multigraphs. A graph is called simple, if each of its edges is contained in E only once, i.e., if the graph does not have parallel edges. An edge joining a vertex to itself, i.e., and edge whose end [vertices are identical, is called a loop.] In both undirected and directed graphs, we may allow the edge set E to contain the same edge several times, i.e., E can be a multiset. If an edge occurs several times in E, the copies of that edge are called parallel edges. Graphs with parallel edges are also called multigraphs. A graph is called simple, if each of its edges is contained in E only once, i.e., if the graph does not have parallel edges. An edge joining a vertex to itself, i.e., an edge whose endvertices are identical, is called a loop.
 Anmerkungen The source is not given. The definitions given here are certainly standard and don't need to be referenced. Nm, however, copied the formulation of those definitions word for word. Sichter (Hindemith), WiseWoman

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A graph is called loop-free if it has no loops.

A graph $G' = (V' ,E')$ is a subgraph of the graph $G = (V, E)$ if $V' \subseteq V$ and $E' \subseteq E$. It is a (vertex-)induced graph if $E'$ contains all edges $e \in E$ that join vertices in $V'$.

Often it is useful to associate numerical values (weights) with the edges or vertices of a graph $G = (V, E)$. The graph having weights is known as weighted graph. Here we only discuss edge weights. Edge weights can be represented as a function $\omega : E \to \mathbb{R}$ that assigns each edge $e \in E$, a weight $\omega(e)$. Depending on the context, edge weights can describe various properties such as strength of interaction, or similarity. One usually tries to indicate the characteristics of the edge weights by the choice of the name of the function. For most purposes, an unweighted graph $G = (V, E)$ is equivalent to a weighted graph with unit edge weights $\omega(e) = 1$ for all $e \in E$.

[Page 8, line 14]

A graph is called loop-free if it has no loops.

[Page 9, line 4-6]

A graph $G' = (V' ,E')$ is a subgraph of the graph $G = (V, E)$ if $V' \subseteq V$ and $E' \subseteq E$. It is a (vertex-)induced subgraph if $E'$ contains all edges $e \in E$ that join vertices in $V'$.

[Page 8, line 16ff]

Weighted graphs. Often it is useful to associate numerical values (weights) with the edges or vertices of a graph $G = (V, E)$. Here we discuss only edge weights. Edge weights can be represented as a function $\omega : E \to \mathbb{R}$ that assigns each edge $e \in E$ a weight $\omega(e)$.

Depending on the context, edge weights can describe various properties such as [...] strength of interaction, or similarity. One usually tries to indicate the characteristics of the edge weights by the choice of the name for the function. [...] For most purposes, an unweighted graph $G = (V, E)$ is equivalent to a weighted graph with unit edge weights $\omega(e) = 1$ for all $e \in E$.

 Anmerkungen The source is not given. The definitions given here are certainly standard and don't need to be referenced. The author however copies not only the definitions but also their formulation word for word. The symbol for the real numbers used as the domain of the weight function is reproduced only as a box in the thesis, indicating that this character was not available in the font used. Sichter (Hindemith), WiseWoman

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A review of key centrality concepts can be found in the papers by Freeman (1978, 1979). This work has contributed significantly to the conceptual clarification and theoretical application of centrality. He provides three general measures of centrality termed “degree”, “closeness”, and “betweenness”. His development was partially motivated by the structural properties of the center of a star graph. [page 2]

A review of key centrality concepts can be found in the papers by Freeman (1979a,b). His work has significantly contributed to the conceptual clarification and theoretical application of centrality. Motivated by the work of Nieminen (1974), Sabidussi (1966), and Bavelas (1948), he provides three general measures of centrality termed

[page 3]

“degree”, “closeness”, and “betweenness”. His development is partially motivated by the structural properties of the center of a star graph.

 Anmerkungen Shortened but otherwise identical. Not marked as a citation, no reference given. Sichter (Graf Isolan), WiseWoman

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A central node was one which was at the center of a number of connections, a node with many direct contacts with other nodes. The simplest and most straight-forward way to measure node centrality, therefore, is by the degrees of various nodes in the graph. The degree, is simply the number of other points to which a node is adjacent. A node is central, then, if it has high degree; the corresponding agent is central in the sense of being well connected or in the thick of things. A central point was one which was 'at the centre' of a number of connections, a point with a great many direct contacts with other points. The simplest and most straightforward way to measure point centrality, therefore, is by the degrees of the various points in the graph. Tle degree, it will be recalled, is simply the number of other points to which a point is adjacent. A point is central, then, if it has a high degree; the corresponding agent is central in the sense of being 'well-connected' or 'in the thick of things'.
 Anmerkungen The source is not given here. Sichter (Hindemith), Bummelchen

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The degree centrality is, e.g., applicable whenever the graph represents something like a voting result. These networks represent a static situation and we are interested in the vertex that has the most direct votes or that can reach most other vertices directly. The degree centrality is a local measure, because the centrality value of a vertex is only determined by the number of its neighbours. The degree centrality is, e.g., applicable whenever the graph represents something like a voting result. These networks represent a static situation and we are interested in the vertex that has the most direct votes or that can reach most other vertices directly. The degree centrality is a local measure, because the centrality value of a vertex is only determined by the number of its neighbors.
 Anmerkungen The source is not mentioned anywhere in the thesis. Note, that this paragraph can also be found in other publications of Nm: Memon, Larsen, Hicks & Harkiolakis (2008) and Memon, Hicks & Larsen (2007). Henrik Legind Larsen is the thesis supervisor and David L. Hicks is the thesis committee chairman. Sichter (Hindemith), Bummelchen

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A degree based measure of node centrality can be extended beyond direct connections to those at various path distances. In this case, the relevant neighbourhood is widened to include the more distant connections of the nodes. A node may, then, be assessed for its local centrality in terms of both direct (distance 1) and distance 2 connections—or, indeed, whatever cut-off path distance is chosen. The principal problem with extending this measure of node centrality beyond distance 2 connections is that, in graphs with even a very modest density, the majority of the nodes tend to be linked through indirect connections at relatively short path distances.

Thus a comparison of local centrality scores at a distance 4 is unlikely to be informative if most of the nodes are connected to most other nodes at this distance.

The degree, therefore, is a measure of local centrality, and a comparison of the degrees of various nodes in a graph can show how well connected the nodes are with their local environments.

This measure of local centrality has one major limitation. That is comparisons of centrality scores can only meaningfully be made among members of the same graph or between graphs that are the same size. The degree of a node depends on, among other things, the size of the graph, and so measure of local centrality cannot be compared when graphs differ significantly in size.

Local centrality is, however, only one conceptualization of node centrality, and Freeman (1979, 1980) has proposed a measure of global centrality based around what he terms the closeness of the nodes.

A degree-based measure of point centrality can be extended beyond direct connections to those at various path distances. In this case, the relevant 'neighbourhood' is widened to include the more distant connections of the points. A point may, then, he assessed for its local centrality in terms of both direct (distance 1) and distance 2 connections or, indeed, whatever cut-off path distance is chosen. The principal problem with extending this measure of point centrality beyond distance 2 connections is that, in graphs with even a very modest density, the majority of the points tend to be linked through indirect connections at relatively short path distances. Thus, comparisons of local centrality wares at distance 4, for example, are [EN 87] unlikely to be informative if most of the points are connected to

[p. 84]

most other points at this distance. [...] The degree, therefore, is a measure of local centrality, and a comparison of the degrees of the various points in a graph can show how well connected the points are with their local environments.

This measure of local centrality has, however, one major limitation. This is that comparisons of centrality mores can only [EN 88] meaningfully be made among the members of the same graph

[p. 85]

or between graphs which are the same size. The degree of a point depends on, among other things, the size of the graph, and so measures of local centrality cannot be compared when graphs differ significantly in size. [...] Local centrality is, however, only one conceptualization of point centrality, and Freeman (1979, 1990) has proposed a measure of global centrality based around what he terms the 'closeness' of the points.

[EN 87] [Bibliography is not available online] [EN 88]

 Anmerkungen Scott is mentioned in the thesis for the first time on page 220. Sichter (Hindemith), Bummelchen, WiseWoman

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We denote the sum of the distances from a vertex u ∈ V to any other vertex in a graph G = (V,E) as the total distance $\sum_{v\in V}d(u,v)$.

The problem of finding an appropriate location can be solved by computing the set of vertices with a minimum total distance.

In SNA literature, a centrality measure based on this concept is called closeness. The focus lies here, for example, on measuring the closeness of a person to all other people in the network. People with a small total distance are considered as more important as those with high total distance. The most commonly employed definition of closeness is the reciprocal of the total distance:

$C_{C}(u)=\frac{1}{\sum_{v\in V}d(u,v)}\qquad (2)$

$C_{C}(u)$ grows with decreasing total distance of u, therefore it is also known as structural index.

We denote the sum of the distances from a vertex u ∈ V to any other vertex

in a graph G = (V,E) as the total distance [FN 2] $\sum_{v\in V}d(u,v)$. The problem of finding an appropriate location can be solved by computing the set of vertices with minimum total distance. [...]

In social network analysis a centrality index based on this concept is called closeness. The focus lies here, for example, on measuring the closeness of a person to all other people in the network. People with a small total distance are considered as more important as those with a high total distance. [...] The most commonly employed definition of closeness is the reciprocal of the total distance

[page 23]

$C_{C}(u)=\frac{1}{\sum_{v\in V}d(u,v)}\qquad (3.2)$

In our sense this definition is a vertex centrality, since cC(u) grows with decreasing total distance of u and it is clearly a structural index.

 Anmerkungen The source is not mentioned anywhere in the thesis Sichter (Hindemith), Bummelchen

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The third measure is called betweenness and is the frequency at which a node occurs on a geodesic that connects a pair of nodes. Thus, any node that falls on the shortest path between other nodes can potentially control the transmission of information or effect exchange by being an intermediary. “It is the potential for control that defines the centrality of these nodes” (Frantz, T. and K. M. Carley, 2005). The third measure is called betweenness and is the frequency at which a point occurs on the geodesic that connects pairs of points. Thus, any point that falls on the shortest path between other points can potentially control the transmission of information or effect exchange by being an intermediary. “It is this potential for control that defines the centrality of these points” (Freeman 1979a: 221).
 Anmerkungen nothing is marked as a citation, the source remains unnamed. Sichter (Graf Isolan), Hindemith

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Let $\delta_{uw}(v)$ denotes the fraction of shortest paths between u and w that contain vertex v:

$\delta_{uw}(v)=\frac{\sigma_{uw}(v)}{\sigma_{uw}}\quad (3)$

where $\sigma_{uw}$ denotes the number of all shortest-paths between s and t. The ratio $\delta_{uw}(v)$ can be interpreted as the probability that vertex v is involved into any communication between u and w. Note, that the measure implicitly assumes that all communication is conducted along shortest paths. Then the betweenness centrality $C_{B}(v)$ of a vertex v is given by:

$C_{B}(v)=\sum_{u\neq v\in V}\sum_{w\neq v\in V}\delta_{uw}(v)\quad (4)$

Any pair of vertices u and w without any shortest path from u to w will add zero to the betweenness centrality of every other vertex in the network.

Let $\delta_{st}(v)$ denote the fraction of shortest paths

between s and t that contain vertex v:

$\delta_{st}(v)=\frac{\sigma_{st}(v)}{\sigma_{st}}\quad (3.12)$

where $\sigma_{st}$ denotes the number of all shortest-path between s and t. Ratios $\delta_{st}(v)$ can be interpreted as the probability that vertex v is involved into any communication between s and t. Note, that the index implicitly assumes that all communication is conducted along shortest paths. Then the betweenness centrality $c_{B}(v)$ of a vertex v is given by:

$c_{B}(v)=\sum_{s\neq v\in V}\sum_{t\neq v\in V}\delta_{st}(v)\quad (3.13)$

[...]

[...] Any pair of vertices s and t without any shortest path from s to t just will add zero to the betweenness centrality of every other vertex in the network.

 Anmerkungen The definitions given here are of course standard and don't require a citation. However, the interpreting and explaining text is taken from the source word for word. The source is not mentioned in the thesis anywhere. Telling mistake: indeed, in his definition Nm writes "where $\sigma_{uw}$ denotes the number of all shortest-paths between s and t.", thus mistakenly referring to the name of the nodes in the original text. Sichter (Hindemith), Bummelchen

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The Figure 1.4 (in Chapter 1) shows an example of a terrorist network, which maps the links between terrorists involved in the tragic events of September 11, 2001. This graph was constructed by Valdis Krebs (2002) using the public data that were available [before, but collected after the event.] Figure 1 shows an example of a terrorist network, which maps the links between terrorists involved in the tragic events of September 11, 2001. This graph was

constructed in [32] using the public data that were available before, but collected after the event.

[32]. Krebs, V.: Mapping networks of terrorist cells. Connections 24, 45–52 (2002)

 Anmerkungen To be continued on the next page Nm/Fragment_107_01 Sichter (Hindemith), Graf Isolan

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[This graph was constructed by Valdis Krebs (2002) using the public data that were available] before, but collected after the event. Even though the information mapped in this network is by no means complete, its analysis may still provide valuable insights into the structure of a terrorist organization. This graph was constructed in [32] using the public data that were available before, but collected after the event. Even though the information mapped in this network is by no means complete, its analysis may still provide valuable insights into the structure of a terrorist organization.

[32]. Krebs, V.: Mapping networks of terrorist cells. Connections 24, 45–52 (2002)

 Anmerkungen The copied text starts on the previous page: Nm/Fragment_106_14 Sichter (Hindemith), Graf Isolan

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According to Kreb’s (2002) analysis, this network had 62 members in total, of which 19 were kidnapers, and 43 assistants: organizers, couriers, financiers, scouts, representatives, coordinators, counterfeiters, etc. Allen (2004) found that successfully functioning large networks typically comprise 25-80 members, with an optimal size between 45 and 50. A close match exists between the results of Allen’s analysis of collaborating networked groups and this particular example of a terrorist group.

Inspection of this network by standard measures of network structure reveals firstly its low connectedness. A member of this network holds only 4.9 connections with others members on average (also known as degree centrality), which means that average members were rather isolated from the rest of the network. The density (which is defined as the number of actual links divided by the number of possible links) of this network is only 0.08, meaning that only 8% of all possible connections in the network really exist.

According to Krebs’ analysis, this wider network had 62 members in total, of which 19 were kidnappers, and 43 assistants: organisers, couriers, financiers, scouts,

counterfeiters etc. Allen found that successfully functioning large networks typically comprise 25-80 members, with optimal size between 45 and 50. Again, a close match exists between the results of Allen’s analysis of collaborating networked groups and this particular example of a terrorist group.

[Page 34]

Inspection of this network by standard measures of network structure [16 – 18] reveals firstly its low connectedness. A member of this network holds only 4,9 connections with other members on average [En 3], which means that average members are rather isolated from the rest of the network. [...] Connectedness measure [EN 4] of this network is only 0,08, meaning that only 8 % of all possible connections in the network really exist.

[EN 3] This means that average degree of nodes is 4,9, where degree of a node represents the number of links coming out of the node.

[EN 4] Connectedness of a given network is the ratio of actually existing number of links in this network and the maximal number of links that would be possible in a network with the same number of nodes, where each node would be linked to each other.

[16] Krebs, V.E.: An Introduction to Social Network Analysis. 2005, http://www.orgnet.com /sna.html,

[17] Wolfe, A.W.: Applications of Network Models – Glossary to Accompany the Course and the Manuscript. 2001, http://luna.cas.usf.edu /~wolfe/glossary.html,

[18] Borgatti, S.P.: Intra-Organizational Networks. Handouts for the course Introduction to Organizational Behavior, 1996, revised 2002, http://www.analytictech.com /mb021/intranet.htm,

 Anmerkungen There is no reference to the source. Note, that at the beginning of chapter 3 on page 93, there is a footnote commenting the title of chaper 3. It says: FN 13: The parts of this chapter are already published in (Memon N, Henrik, L. L. 2006a, 2006b, 2006c, 2006d) However, the source Penzar et al. (2005) has been published before any of those publications. Sichter (Hindemith), Bummelchen

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In spite of low connectedness, however, the nodes of this network

are relatively close. The average closeness of nodes is 0.35. Betweenness as stated above is another important measure in SNA and it indicates a node’s importance for communication among other nodes. The average betweenness of this network is 0.032, indicating relatively high average redundancy. However the betweenness of 40 nodes is in fact less than 1% and only 6 nodes have betweenness higher than 10%. These 6 nodes are critical for information flow, especially one with betweenness of almost 0.589, meaning that almost 60% of communication paths among other nodes pass through this central node. The node represents Mohamed Atta (node # 33); the leading organizer of the attack whose central position in the network is confirmed by other centrality indicators as well.

Distribution of degrees of nodes (see Section 3.4.1) is particularly interesting. Degrees of nodes are exponentially distributed: the degree of most of the nodes is small, while few nodes have high degree (see Figure 3.5 and Figure 3.6).

In spite of the low connectedness, however, nodes of this network are relatively close. [...] the average closeness [EN 5] of nodes is 0,35. Betweenness [FN 6] is another important measure in social network analysis and it indicates a node’s importance for communication among other nodes. The average betweenness of this network is 0,032, indicating relatively high average redundancy. However, betweenness of forty nodes is in fact less than 1 %, and only six nodes have betweenness higher than 10 %. These six nodes are obviously critical for information flow, especially the one with betweenness of almost 60 %, meaning that almost 60 % of communication paths among other nodes pass through this central node. This node represents Mohamed Atta, the leading organiser of the attack whose central position in the network is confirmed by other centrality indicators as well.

[...]

Distribution of degrees of nodes is particularly interesting. Degrees of nodes are exponentially distributed: the degree of most nodes is small, while only few nodes have high degree (Fig. 6).

 Anmerkungen There is no reference to the source. Note, that at the beginning of chapter 3 on page 93, there is a footnote commenting the title of chaper 3, which says: FN 13: The parts of this chapter are already published in (Memon N, Henrik, L. L. 2006a, 2006b, 2006c, 2006d) However, the source Penzar et al. (2005) has been published before any of those publications. Sichter (Hindemith), Bummelchen

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[This property characterises] the so called scale free networks (Watts, D.J., 2003; Kreisler, H., 2003). Scale free networks (see Section 3.5) form spontaneously, without needing a particular plan or interventions of central authority. Nodes that are members of the network for a longer time, that are better connected with other nodes, and that are more significant for a functioning network, are also more visible to new members, so that the new members spontaneously connect more readily to such nodes than other, relatively marginal ones.

[FIGURE: identical to corresponding figure in Source]

Figure 3.6. Distribution of degree of nodes in the network (see Figure 1.4) of kidnappers and their supporters.

On the pattern of scale free networks, the Al Qaeda’s Training Manual (2001) states: “The cell or cluster methods should be organized in a way that a group is composed of many cells whose members do not know each other, so that if a cell member is caught, other cells would not be affected, and work would proceed [normally”.]

This property characterises the so-called scale-free networks [19, 20; pp.104-111][EN 8], [...]. Scale-free networks form spontaneously, without needing a particular plan or interventions of a central authority. Nodes that are members of the network for a longer time, that are better connected with other nodes, and that are more significant for network’s functioning, are also more visible to new members, so that the new members spontaneously connect more readily to such nodes than to other, relatively marginal ones.

[FIGURE]

Figure 6. Distribution of degrees of nodes in the network of kidnappers and their supporters

[Page 35]

[...] Al-Qaeda’s Training Manual states: “Cell or cluster methods should be adopted by the Organization. It should be composed of many cells whose members do not know one another, so that if a cell member is caught, the other cells would not be affected, and work would proceed normally.” [12; Third Lesson].

[12] Al Qaeda Training Manual. US Department of Justice, http://www.usdoj.gov/ag/ trainingmanual.htm and http://www.pbs.org/wgbh/pages/ frontline/shows/network/alqaeda/manual.html,

[18] Borgatti, S.P.: Intra-Organizational Networks. Handouts for the course Introduction to Organizational Behavior, 1996, revised 2002, http://www.analytictech.com/ mb021/intranet.htm,

[19] Li, L.; Anderson, D.; Tanaka R.; Doyle J.C.; Willinger, W.: Towards a Theory of Scale- Free Graphs: Definition, Properties, and Implications. Technical Report CIT-CDS-04-006, Engineering and Applied Sciences Division, California Institute of Technology, Pasadena, 2004, http://arxiv.org/PS_cache/ cond-mat/pdf/0501/0501169.pdf,

[20] Watts, D.J.: Six Degrees – The Science of a Connected Age. W. W. Norton & Company, New York, London, 2003,

 Anmerkungen There is no reference to the source. Note, that at the beginning of chapter 3 on page 93, there is a footnote commenting the title of chaper 3. It says: FN 13: The parts of this chapter are already published in (Memon N, Henrik, L. L. 2006a, 2006b, 2006c, 2006d) However, the source Penzar et al. (2005) has been published before any of those publications. Sichter (Hindemith), Bummelchen

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In graph theory a number of measures have been proposed to characterize networks. However, three concepts are particularly important in contemporary studies of the topology of complex networks: degree distribution, clustering coefficient, and average path length (Albert, R., and Barabasi, A.L. (2002); Dorogovtsev, S. N., and Mendes, J. F. F. (2002); Newman, M. E. J. (2003)).

3.4.1 Degree Distribution

The degree $k_i$ is the number of edges connecting to the i$^{th}$ vertex. The vertex degree is characterized by a distribution function P(k), which gives the probability that a randomly selected vertex has k edges. Recent studies show that several complex networks have a [heterogeneous topology, i.e., some vertices have a very large number of edges, but the majority of the vertices only have a few edges.]

In graph theory, a number of measures have been proposed to characterize networks. However, three concepts are particularly important in contemporary studies of the topology of complex networks: degree distribution, clustering coefficient, and average path length. [EN 1-3]

2.2.1. Degree Distribution

The degree $k_i$ is the number of edges connecting to vertex i. The vertex degree is characterized by a distribution function P(k), which gives the probability that a randomly selected vertex has k edges. Recent studies show that several complex networks have a heterogeneous topology, i.e., some vertices have a very large number of edges, but the majority of the vertices only have a few edges.

[EN 1]. Albert, R., & Barabási, A.-L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74, 47–97.

[EN 2]. Dorogovtsev, S. N., & Mendes, J. F. F. (2002). Evolution of networks. Advances in Physics, 51, 1079–1187.

[EN 3]. Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review, 45, 167–256.

 Anmerkungen Nearly identical, though the source is not mentioned anzwhere. Sichter (Graf Isolan), Hindemith

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[Recent studies show that several complex networks have a] heterogeneous topology, i.e., some vertices have a very large number of edges, but the majority of the vertices only have a few edges. That is, the degree distribution follows a power law $P(k)\cup k^{-\gamma}$ for large k(i.e., $P(k)/ k^{-\gamma} \to 1$ when $k\to \infty$). The average degree (k) of a graph with N vertices and M edges is (k) = 2M / N.

3.4.2 Clustering Coefficient

Many complex networks exhibit an inherent tendency to cluster. In social networks this represents a circles of friends in which every member knows each other. The clustering coefficient is a local property capturing “the density” of triangles in a graph, i.e., two vertices that both are connected to a third vertex are also directly connected to each other. An $i^{th}$ vertex in a network has $k_i$ edges that connects it to $k_i$ other vertices. The maximum possible number of edges between the $k_i$ neighbours is ${k_i \choose 2}=k_i (k_i-1) / 2.$ The clustering coefficient of $i^{th}$ vertex is defined as the ratio between the number $M_i$ of edges that actually exist between these $k_i$ vertices and the maximum possible number of edges, i.e., $C_i = 2 M_i / k_i(k_i-1).$ The clustering coefficient of the whole network

$C = (1/N)\sum_{i=1}^{n}{\mathcal C}_{i} \sum_{i=1}^{n} C_i$

3.4.3 Average Path Length

The distance $l_{uv}$ between two vertices u and v is defined as the number of edges along the shortest path connecting them. The average path length $l = (l_{uv}) = [1 / N (N - 1)] \sum_{u\neq v\in V}l_{uv}$ is a measure of how a network is scattered. Sometimes, the diameter d of a graph is defined as the maximum path length between any two connected vertices in the graph. However, in other situations the concept diameter relate to the average path length, i.e., d = l.

[p. 956]

Recent studies show that several complex networks have a heterogeneous topology, i.e., some vertices have a very large number of edges, but the majority of the vertices only have a few edges. That is, the degree distribution follows a power law $P(k)\cup k^{-\gamma}$ for large k(i.e., $P(k)/ k^{-\gamma} \to 1$ when $k\to \infty$). The average degree (k) of a graph with N vertices and M edges is (k) = 2M/N.

2.2.2. Clustering Coefficient

Many complex networks exhibit an inherent tendency to cluster. In social networks this represents circles of friends in which every member knows each other. The clustering coefficient is a local property capturing “the density” of triangles in the graph, i.e., two vertices that both are connected to a third vertex are also directly connected to each other. A vertex i in the network has $k_i$ edges that connects it to

[p. 957]

$k_i$ other vertices. The maximum possible number of edges between the ki neighbors is ${k_i \choose 2}=k_i(k_i-1)/2.$ The clustering coefficient of vertex i is defined as the ratio between the number $M_i$ of edges that actually exist between these $k_i$ vertices and the maximum possible number of edges, i.e., $C_i = 2 M_i / k_i(k_i-1).$ The clustering coefficient of the whole network $C = (1/N)\sum_i {\mathcal C}_i.$

2.2.3. Average Path Length

The distance $l_{uv}$ between two vertices u and v is defined as the number of edges along the shortest path connecting them. The average path length $l = (l_{uv}) = [1 / N (N - 1)] \sum_{u\neq v\in V}l_{uv}$ is a measure of how the network is scattered. Sometimes, the diameter d of a graph is defined as the maximum path length between any two connected vertices in the graph. However, in other situations the concept diameter relate to the average path length, i.e., d = l.

 Anmerkungen The copying process continues with Nm introducing an unfortunate mistake in the formula for the clustering coefficient. Apart from this mistake, both texts are nearly identical. Sichter (Graf Isolan), Hindemith

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[The] so-called small-world property appears to characterize many complex networks. Despite their often-large size, there is a relatively short path between any two vertices in a network: the average shortest paths between a pair of vertices scales as the logarithm of the number of vertices.

3.5 GRAPHS AS MODELS OF REAL-WORLD NETWORKS

The study of networks, and in particular the interest in the statistical measures of the topology of networks (see section 3.4), has given birth to three main classes of network models. The random graph was introduced by Erdos and Renyi in the late 1950s and is one of the earliest theoretical models of a network (Bollobas, B., 1985). This is the easiest model to analyze mathematically and it can serve as a reference for randomness. Watts and Strogatz introduced the so called small world model in 1998 (Watts, D. J., and Strogatz, S. H., 1998). This model combines high clustering and a short average path length.

In 1999, Barabasi and Albert (BA) addressed the origin of the power-law degree distribution, evident in many real networks, with a simple model (also known as the scale-free network model) that put the emphasis on how real networks evolve (Albert, R., Jeong, H., and Barabasi, A.L., 2000).

The so-called small-world property appears to characterize many complex networks. Despite their often-large size, there is a relatively short path between any two vertices in the network: the average shortest paths between

a pair of vertices scales as the logarithm of the number of vertices.

2.3. Graphs as Models of Real-World Networks

2.3.1. Theoretical Network Models

The study of networks, and in particular the interest in the statistical measures of the topology of networks (previous section), has given birth to three main classes of network models. The random graph was introduced by Erdös and Rènyi in the late 1950s and is one of the earliest theoretical models of a network. [EN 12] This is the easiest model to analyze mathematically and it can serve as a reference for randomness. Watts and Strogatz introduced the so-called small world model in 1998. [EN 4] This model combines high clustering and a short average path length. In 1999, Barabási and Albert (BA) addressed the origin of the power-law degree distribution, evident in many real networks, with a simple model (also known as the scale-free network model) that put the emphasis on how real networks evolve. [EN 13]

---

[EN 4]. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature, 393, 440–442.

[EN 12]. Bollobás, B. (1985). Random Graphs. London: Academic Press.

[EN 13]. Albert, R., Jeong, H., & Barabási, A.-L. (2000). Error and attack tolerance of complex networks. Nature, 406, 378–382.

 Anmerkungen The third page of text which is identical to Holmgren (2006). No source given. Sichter (Graf Isolan), Hindemith

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Three models have been used to distinguish complex networks: random graph model, small-world model, and scale-free model (Albert and Barabasi, 2002). It is to note that most complex systems are not random but are governed by some well known principles encoded in the topology of the networks. Three models have been employed to characterize complex networks: random graph model, small-world model, and scale-free model (Albert & Barabasi, 2002). Most complex systems are not random but are governed by certain organizing principles encoded in the topology of the networks.
 Anmerkungen Nm writes sentences that can also be found in a book of one of Nm's referees. Moreover, in view of what Nm has written in the previous paragraphs the first sentence with its reference does not make much sense, since it is more or less a repetition of a list given further above. Sichter (Graf Isolan), Hindemith

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It is worthwhile to mention here, that a small-world network has a significantly larger clustering coefficient while the network has small average path length. The large clustering coefficient point outs that there is a high tendency for nodes to form communities and groups. On the other hand, scale-free networks are characterized by the power-law degree distribution, meaning that while a large number of nodes in the network have just a few links, a small fraction of the nodes have a large number of links. It is believed that scale-free networks evolve following the selforganizing principle, where growth and preferential attachment play a key role for the emergence of the power law degree distribution (Chen, Hsinchun, 2006). [p. 98]

A small-world network has a significantly larger clustering coefficient than its random model counterpart while maintaining a relatively small average path

[p. 99]

length. The large clustering coefficient indicates that there is a high tendency for nodes to form communities and groups. Scale-free networks, on the other hand, are characterized by the power-law degree distribution, meaning that while a large number of nodes in the network have just a few links, a small fraction of the nodes have a large number of links. It is believed that scale-free networks evolve following the self-organizing principle, where growth and preferential attachment play a key role for the emergence of the power-law degree distribution.

 Anmerkungen It is not clear that this paragraph is an - if you know the source - obvious citation. The reference "(Chen, Hsinchun, 2006)" indeed refers to one possible source at hand even though it is a multi authored paper (the wording is close to the one in Chen's book, which is presented here). Chen was one of the referees for this thesis, so one expects that he, at least, is - more or less - correctly cited. This is not the case. Sichter (Graf Isolan), Bummelchen

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Although the topological properties of these networks have been

discovered, the structures of terrorist networks are largely unknown due to the difficulty of collecting and accessing reliable data (Krebs, 2001). Do terrorist networks share the same topological properties with other types of networks? Do they follow the same organizing principle? How do they achieve efficiency under constant surveillance and threat from authorities? (Chen, Hsinchun, 2006).

Although the topological properties of these networks have been discovered,

the structures of dark (covert, illegal) networks are largely unknown due to the difficulty of collecting and accessing reliable data (Krebs, 2001). Do dark networks share the same topological properties with other types of networks? Do they follow the same organizing principle? How do they achieve efficiency under constant surveillance and threat from authorities?

 Anmerkungen Straight from the book of one of the referees, but the citation is not marked as such. The reference is to one of the papers of the referee, but the wording is nearly identical to what can be found in the book. Sichter (Graf Isolan), Bummelchen

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This study applied the small-world network metrics of Watts & Strogatz (1998) to Figure 1.4 and tabulated in Table 3.1. One of the key metrics in the small-world model is the average path length, for individuals and for the network overall (Krebs, V., 2005). A good score for an individual means that he/she is close to all of the others in a network – they can reach others quickly without going through too many intermediaries. A good score for the whole network indicates that everyone can reach everyone else easily and quickly. The shorter the information paths for everyone, the quicker the information arrives and the less distorted it is when it arrives. Another benefit of multiple short paths is that most members of the network have good visibility into what is happening in other parts of the network – a greater awareness. They have a wide network horizon which is useful for combining key pieces of distributed [intelligence.] We apply the small-world network metrics of Watts & Strogatz to Figures 1, 2, and 3 above. One of the key metrics in the small-world model is the average path length, for individuals and for the network overall. A good score for an individual means that he/she is close to all of the others in the network -- they can reach others quickly without going through too many intermediaries. A good score for the whole group indicates that everyone can reach everyone else easily and quickly. The shorter the information paths for everyone, the quicker the information arrives and the less distorted it is when it arrives. Another benefit of multiple short paths is that most members of the network have good visibility into what is happening in other parts of the network. They have a good network horizon which is useful for combining key pieces of distributed intelligence.
 Anmerkungen Taken straight from the world wide web; the reference is given - still it is by no means clear that it mostly is a word-for-word citation. Note also that the copied text continues after the reference to the source. Sichter (Graf Isolan) Agrippina1

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In an environment where it is difficult to distinguish signal from noise, it is important to have many perspectives involved in the sense-making process (Krebs, V., 2005). In an environment where it is difficult to distinguish signal from noise, it is important to have many involved in the sense-making process.
 Anmerkungen source given, but the citation is not marked as such; continuation from the previous page Sichter (Graf Isolan) Agrippina1

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It would be noted that this research study found that members in the 9/11 terrorist network (Figure 1.4) are extremely close to their leaders. The terrorists in the network are on average only 1.79 steps away from Mohamed Atta, meaning that Mohammed Atta’s (node 33) command can reach an arbitrary member through only two mediators (approximately). Despite its small size (62), the average path length is 3.01, [...] [p. 99]

We found that members in the criminal and terrorist networks are extremely close to their leaders. The terrorists in the GSJ network are on average only 2.5 steps away from bin Laden, meaning that bin Laden’s command can reach an arbitrary member

[p.100]

through only two mediators. [...] Despite its small size (80), the average path length is 4.70, [...]

 Anmerkungen different example, but the words are in large parts identical to a section in one of Nm's referees' book. Sichter (Graf Isolan), Bummelchen

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The other small-world topology, high clustering coefficient, is also present in this network. The clustering coefficient of this network is 0.49 significantly high. Previous studies have also shown the verification of groups in this network. In these groups, members be likely to have solid (dense) and stronger relations with one another. The communication between group members becomes more

efficient, making a crime/ terrorist plan or an attack easier to plan, organize, and execute (Chen, Hsinchun, 2006).

In addition, this type of network is a scale-free system (as discussed above). The degree distribution decays slowly for small degrees than for that of other types of networks, which indicates a higher frequency for small degrees.

The other small-world topology, high clustering coefficient, is also present in the GSJ network (see Table 2). The clustering coefficient of the GSJ network is significantly higher than its random graph counterpart. Previous studies have also shown the evidence of groups and teams inside this kind of illegal networks [12, 33, 43, 44]. In these groups and teams, members tend to have denser and stronger relations with one another. The communication between group members becomes more efficient, making an attack easier to plan, organize, and execute [27].

Moreover, the GSJ network is also a scale-free system. [...] The degree distribution decays much more slowly for small degrees than for that of other types of networks, indicating a higher frequency for small degrees.

 Anmerkungen different example, but the words are in large parts identical Sichter (Graf Isolan), Bummelchen

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The topological properties of 9/11 terrorist network is reported in the table above. [...] It is hoped that this study not only to contribute to general knowledge of the topological properties of complex systems (particularly terrorist networks) in a hostile environment but also to provide law enforcement and intelligence agencies with insights regarding destabilizing strategies strategies. We report in this study the topological properties of several covert criminal- or terrorist-related networks. We hope not only to contribute to general knowledge of the topological properties of complex systems in a hostile environment but also to provide authorities with insights regarding disruptive strategies.
 Anmerkungen Taken out of context and put at the end of his own example of research. Sichter (Graf Isolan), Bummelchen

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3.6.1 The Efficiency of a Network

[...] (Latora and Marchiori, 2004). The network efficiency E(G) is a measure to quantify how efficiently the nodes of a network exchange information. To define efficiency of a network G, first we calculate the shortest path lengths $d_{ij}$ between the ith and the jth nodes. Let us now suppose that every node sends information along the network, through its links. The efficiency in the communication between the ith node and the jth node is inversely proportional to the shortest distance: when there is no path in the graph between the ith and the jth nodes, we get $d_{ij}= + \infty$ and efficiency becomes zero. Let N be known as the size of the network or the numbers of nodes in the graph, the average efficiency of the graph (network) of G can be defined as:

$C_{eff}=E(G)=\frac{1}{N(N-1)}\sum_{i\ne j \in G} \frac{1}{d_{ij} }\quad {\mathbf (6)}$

The above formula gives a value of $C_{eff} E$ in the interval of [0, 1].

2. The efficiency of a network

[...]

The network efficiency E, is a measure introduced in Refs. [5,6] to quantify how efficiently the nodes of the network exchange information.

[...]

To define the efficiency of G first we have to calculate the shortest path lengths $\{d_{ij}\}$ between two generic points i and j.

[...]

Let us now suppose that every vertex sends information along the network, through its edges. We assume that the efficiency $\epsilon_{ij}$ in the communication between vertex i and j is inversely proportional to the shortest distance: [...] when there is no path in the graph between i and j we get $d_{ij}= + \infty$ consistently $\epsilon_{ij}=0.$ Consequently the average efficiency of the graph G can be defined as [12]:

$E({\mathbf G})=\frac{\sum_{i\ne j \in G}\epsilon_{ij} }{N(N-1)}=\frac{1}{N(N-1)}\sum_{i\ne j \in G} \frac{1}{d_{ij} }.\quad {(1)}$

Such a formula (1) gives a value of E that can vary in the range $[0,\infty [$,

[...]

 Anmerkungen Though the source is given, there is no hint that the text following the reference is taken nearly word-for-word from the source (with some shortening). Also, it makes no sense to speak of the "ith" and "jth" node as there is no linear order on a graph. The nodes are just referred to as "i" and "j", as in the source. At the end, Nm produces a mathematical mistake by leaving out too much. Sichter (Graf Isolan), WiseWoman

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3.7.2 Case 2: Riyadh Bombing Terrorists Network [FN 19]

The Riyadh bombings took place on May 12, 2003, in Riyadh, Saudi Arabia. The bombing involved suicide attacks, attributed to Al Qaeda, were the first of several "spectacular attacks" carried out by that group in 2003, and reported as the terrible attack on Americans that year. In the attack 35 people were killed, and over 160 injured.

It is reported that on May 12, when much of Riyadh was asleep, four vehicles drove through Riyadh (two cars, a pickup, and an SUV). Two carried heavily armed assault teams and three of them were packed with explosives. Their targets were three compounds: The Dorrat Al Jadawel, the Al Hamra Oasis Village, and the Vinnell Corporation Compound. It is very important to point that all compounds contained large numbers of Americans and Westerners.

It is reported that around 11:15 PM, a car packed with explosives and five or six terrorists, attempted to enter to the Jadawel compound's back gate area. As the guards approached to check the vehicle, the terrorists abruptly opened fire, killing one Saudi Air Force policeman and one un-armed Saudi civilian security guard. The attackers’ bunched gunfire violently as they attacked the inner compound gate, injuring two other security guards, one of whom managed to secure the gates before fleeing. While the terrorists were still attempting to get inside the compound, their massive explosive suddenly exploded as reported on the media, killing all of the terrorists and a Filipino worker.

At the Al Hamra Oasis Village and the Vinnell Corp. compound, the terrorists shot the security guards outside the compound gates. After that they opened the gates with the security controls and a second team of terrorists drove their trucks into the compounds.

---

[FN 19] The most of the text is taken from http://en.wikipedia.org/wiki/Riyadh_Compound_Bombings

The Riyadh compound bombings took place on May 12, 2003, in Riyadh, Saudi Arabia. These suicide attacks, attributed to al-Qaeda, were the first of several "spectacular attacks" carried out by that group in 2003, and the deadliest attack on Americans that year. Altogether, some 35 people were killed, and over 160 wounded.

[...]

Late on May 12, while much of Riyadh was asleep, four vehicles drove through Riyadh; two cars, a pickup, and an SUV. Two carried heavily armed assault teams and three of them were packed with explosives. Their targets were three compounds: The Dorrat Al Jadawel, a compound owned by MBI International and Partners, the Al Hamra Oasis Village, and the Vinnell Corporation Compound, a compound owned by a Virginia-based defense contractor that was training the Saudi National Guard. All contained large numbers of Americans and Westerners.

Around 11:15 PM, a car packed with explosives and five or six terrorists, quietly attempted to gain entry to the Jadawel compound's back gate area. As the guards approached to inspect the vehicle, the terrorists suddenly opened fire, immediately killing one Saudi Air Force policeman and one unarmed Saudi civilian security guard. The attackers sprayed gunfire wildly as they assaulted the inner compound gate, wounding two other unarmed security guards, one of whom managed to secure the gates before fleeing. While the terrorists were still attempting to get inside the compound, their massive explosive charge suddenly detonated, killing all of the attackers and a Filipino worker.

At the Al Hamra Oasis Village and the Vinnell Corp. compound, the assault teams shot the security guards outside the compound gates. They then opened the gates with the security controls and a second team drove their trucks into the compounds.

 Anmerkungen "most of" appears to mean that everything on this page has been taken from the Wikipedia entry as existed on 11 February 2007 with only some phrases and words having been changed by Nm Sichter (Graf Isolan), WiseWoman

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[As] they fired roughly, they shouted phrases like "God is Great!". Then they exploded both of their bombs, destroying the compounds.

It was reported that at least 26 people died, including 09 US citizens. The fatalities contained seven Saudis, three Filipinos, two Jordanians, and one each from Australia, Britain, Ireland, Lebanon and Switzerland.

It was also mentioned that, nine suicide bombers died, bringing the entire fatalities from the attacks to 35. In the incident more than 160 other people were injured, including more than two dozen US Citizens.

As they fired wildly, they screamed phrases like "God is Great!". They then detonated both of their bombs, devastating the compounds.

Altogether at least 26 people died, including nine Americans. The nationalities of the other dead were seven Saudis, three Filipinos, two Jordanians, and one each from Australia, Britain, Ireland, Lebanon and Switzerland. In addition, nine suicide bombers died, bringing the entire toll from the attacks to 35. More than 160 other people were injured, including more than two dozen Americans.

 Anmerkungen continuation from previous page Sichter (Graf Isolan), Bummelchen

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 Anmerkungen A barely concealed mostly verbatim adaption of what can be found in Freeman (1980). Where Nm leaves the path of the original text the definition becomes nearly incomprehensible and mathematically unsound (although the definition of a path given by Freeman also leaves a bit to be desired). Sichter (Graf Isolan), Hindemith

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The dependence centrality is defined as the degree to which a node, u, must depend upon another, v, to relay its messages along geodesics to and from all other reachable nodes in the network. Thus, for a network containing n nodes [...] Now we can define pair-dependency as the degree to which a point, pi, must depend upon another, pj, to relay its messages along geodesics to and from all other reachable points in the network. Thus, for a network containing n points, [...]
 Anmerkungen The interpretation of the mathematical definitions is word-for-word the same (although the definitions differ actually). Sichter (Graf Isolan), Hindemith

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Each entry in D is an index of the degree to which the node designated by the row of a matrix must depend on the vertex designated by the column to relay messages to and from others. Thus D captures the importance of each node as gatekeeper with respect to every other node—facilitating or perhaps inhibiting its communication. Each entry in D is an index of the degree to which the point designated by the row of the matrix must depend on the point designated by the column to relay messages to and from others. Thus D captures the importance of each point as a gatekeeper with respect to each other point - facilitating or perhaps inhibiting its communication.
 Anmerkungen Even though nearly identical, a reference to the source is completely missing. Sichter (Graf Isolan), Hindemith

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Above Section proposed, developed and illustrated a new measure of centrality called dependence centrality, which may be applied to terrorist networks. This measure reflects the information contained in the shortest paths in a network. [page 26]

We have proposed, developed and illustrated by the use of examples a new measure of centrality called information, which may be applied to

[page 27]

nondirected networks. This measure reflects the information contained in all possible paths in a network.

 Anmerkungen adapted to fit the theme of the thesis, otherwise identical; nothing is marked as a citation, no reference given. Sichter (Graf Isolan), Hindemith

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This chapter attempted to illustrate the calculation of centralities to these prototypical situations. However we regard our efforts in this direction as only a beginning. We have attempted to illustrate the calculation of centralities to these prototypical situations. However we regard our efforts in this direction as only a beginning.
 Anmerkungen This piece is taken word-for-word from the conclusion of the paper which functioned as an unreferenced source. The sentences following will be taken from one of the second of the paper's introductory chapters. Sichter (Graf Isolan), Hindemith

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It is possible that information will take a more circuitous route either by random communication or may be intentionally channelled through many intermediaries in order to “hide” or “shield” information in a way not captured by geodesic paths. These considerations raise questions as to how to include all possible paths in a centrality [measure.] It is quite possible that information will take a more circuitous route either by random communication or may be intentionally channeled through many intermediaries in order to “hide” or “shield” information in a way not captured by geodesic paths. These considerations raise questions as to how to include all possible paths in a centrality measure.
 Anmerkungen This is supposed to be a part of a summary, which Nm did for this chapter. But in fact it stems verbatim from the unnamed source. This section will appear a second time in Nm's thesis as Nm/Fragment_242_09. Sichter (Graf Isolan), Hindemith

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After tragic terrorist attacks by hijacked airlines on New York and

Washington in September 2001 the interest for Al Qaeda in public and media rose immediately. Experts and analysts all over the world started to offer various explanations of Al Qaeda’s origins, membership recruitment, modes of operation, as well as of possible ways of its disruption. Journalists in search of hot topics took over and publicized most of the publicly available materials, often revising them further and making them even more exciting and attractive for wide audiences.

One could thus read or hear that Al Qaeda is “a net that contains independent intelligence”, that it “functions as a swarm”, that it “gathers from nowhere and disappears after action”, that it is “an ad hoc network”, “an atypical organization”, extremely hard to destroy, especially by traditional anti-terrorist or counterterrorist methods.

After catastrophic terrorist attacks by kidnapped airlines on New York and Washington in September 2001 the interest for al-Qaeda terrorist organisation in public and media rose immediately. Experts and analysts all over the world started to offer various explanations of al-Qaeda’s origins, membership recruitment, modes of operation, as well as of possible ways of its disruption. Journalists in search of hot topics took over and publicized most of the publicly available materials, often revising them further and making them even more intriguing and attractive for wide audiences.

One could thus read or hear that al-Qaeda is “a net that contains independent intelligence”, that it “functions as a swarm”, that it “gathers from nowhere and disappears after action”, that it is “an ad hoc network”, “an atypical organisation”, extremely hard to destroy, especially by traditional anti-terrorist methods.

 Anmerkungen Very minor changes. No reference to the source. Note FN 22 (commenting the title of chapter 5): "The parts of this chapter are already published in Memon N., Larsen Henrik Legind 2006c, 2006d, 2007c and Memon N., Qureshi A.R. (2005)." However, Penzar et al. (2005) has been received in May 2005, Memon & Quereshi (2005) has been published in November, Sichter (Hindemith) Agrippina1

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Al Qaeda has evolved from a centrally directed organization into a worldwide franchiser of terrorist attacks (Grier P., 2005). Since war in Afghanistan, which significantly degraded Osama bin Laden’s command and control, Al Qaeda does appear to have become increasingly decentralized. It is now seen by many as more of a social [movement than coherent organization (Wikotorowicz Q., 2001).] According to most counterterrorism analysts today, al-Qa’ida has evolved from a centrally directed organization into a worldwide franchiser of terrorist attacks. [FN 7] Indeed, since the war in Afghanistan, which significantly degraded bin Laden’s command and control, al-Qa’ida has become increasingly decentralized, and is seen by some as more of a “movement” than any other form of organization.

[FN 7] Peter Grier, “The New Al Qa’ida: Local Franchiser,” Christian Science Monitor (11 July 2005). Online at: http://www.csmonitor.com/2005/0711/p01s01-woeu.html.

 Anmerkungen The source is dated February 14, 2006. According to Nm [FN 22] "parts of this chapter are already published in Memon N., Larsen Henrik Legind 2006c, 2006d, 2007c and Memon N., Qureshi A.R. (2005)." This section appeared in Memon N., Larsen Henrik Legind 2006c in the Proceedings of the second International Conference, ADMA 2006, which was held in August 2006. Thus the unnamed source predates the writing of this section of Nm's thesis. Sichter (Graf Isolan), Bummelchen

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[It is now seen by many as more of a social] movement than coherent organization (Wikotorowicz Q., 2001).

Al Qaeda did not decide to decentralize until 2002, following the removal of the Taliban from Afghanistan and the arrest of a number of key Al Qaeda leaders including Abu Zubaydhah, Al Qaeda’s Dean of students, Ramzi bin Al Shibh, the organizer of the Hamburg cell of 9/11 hijackers, Khalid Sheikh Mohammed, the mastermind of 9/11 and the financier of the first World Trade Center attack, and Tawfiq Attash Kallad, the master mind of the USS Cole attack.

In response these and other key losses, Al Qaeda allegedly convened a strategic summit in northern Iran in November 2002, at which the group’s consultative council decided that it could no longer operate as a hierarchy, but instead would have to decentralize (Joseph Felter et al., 2005).

[p. 8]

[...] and is seen by some as more of a “movement” than any other form of organization.

[...]

[p. 9]

Indeed, several years ago al-Qa’ida’s leaders recognized that the achievement of their ultimate goals and objectives required a more decentralized, networked approach. In 2001, following the ouster of the Taliban from Afghanistan, a number of al-Qa’ida leaders suddenly found themselves in detention centers facing long months of interrogation. Abu Zubaydah, al-Qa’ida’s “dean of students,” [...]. Ramzi Bin al Shibh, the organizer of the Hamburg, Germany cell that formed the core of the 9/11 hijackers, [...] Khalid Sheik Mohammed, the mastermind of 9/11 and the financier of the first World Trade Center attack, [...] Tawfiq Attash Kallad, the mastermind of the USS Cole attack, [...] In response to the loss of key leaders, al-Qa’ida allegedly convened a strategic summit in northern Iran in November 2002, at which the group’s consultative council came to recognize that it could no longer exist as a hierarchy, but instead would have to become a decentralized network [...][FN 10]

[FN 10] Robert Windrem, 2005.

 Anmerkungen In spite of some paraphrasing this remains a collage of various bits and pieces of various lengths from the unnamed source from West Point's Combating Terrorism Center. Nothing is marked as a citation. Both sources, (Wikotorowicz Q., 2001) as well as (Joseph Felter et al., 2005) are not to be found in Nm's list of references. However, "Felter et al. 2005" might refer to a version of the source, as "Joe Felter" led its large team of authors. Sichter (Graf Isolan), Bummelchen

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Hierarchy, as one common feature of many real world networks, attracts special attentions in recent years (Ravasz, E., A. L., Barabasi, 2003; Costa, L. D. F., 2004; Trusina, A. et al, 2004, Variano, E. A. et al, 2004). Hierarchy is one of the key aspects of a theoretical model to capture statistical characteristics of terrorist networks.

In literature, several concepts are proposed to measure the hierarchy in a network, such as the hierarchical path (Trusina, A. et al, 2004), the scaling law for the clustering coefficients of nodes in a network ( Ravasz, E., A. L., Barabasi, 2003), etc. These measures can tell us the existence and extent of hierarchy in a network. We address herein another problem how to construct hidden hierarchy of terrorist networks (which are known as horizontal networks).

Hierarchy, as one common feature for many real world networks, attracts special attentions in recent years [9-12]. [...] Hierarchy is one of the key aspects of a theoretical model [9,13] to capture the statistical characteristics of a large number of real networks, [...]

In literature, several concepts are proposed to measure the hierarchy in a network, such as the hierarchical path [10], the scaling law for the clustering coefficients of the nodes [9], the hierarchical components/degree [11], etc. These measures can tell us the existence and the extent of hierarchy in a network. We address herein another problem, that is, how to reconstruct the hierarchical structure in a network.

[9] E. Ravasz and A. -L. Barabasi, Phys. Rev. E 67, 026112(2003).

[10] A. Trusina, S. Maslov, P. Minnhagen and K. Sneppen, Phys. Rev. Lett. 92, 178702(2004).

[11] L. D. F. Costa, Phys. Rev. Lett. 93, 098702(2004).

[12] E. A. Variano, J. H. McCoy and H. Lipson, Phys. Rev. Lett. 92,188701(2004).

[13] Tao Zhou, Gang Yan and Binghong Wang, Phys. Rev. E 71, 046141 (2005).

 Anmerkungen Only minor adjustments. In particular also all the literature references are taken from Yang et al. (2005). Note: the footnote 22, commenting the title of chapter 5 on page 146, reads as follows: "22 The parts of this chapter are already published in Memon N., Larsen Henrik Legind 2006c, 2006d, 2007c and Memon N., Qureshi A.R. (2005)." However, Memon N., Qureshi A.R. (2005) was published in November 2005 (see here), whereas Yang et al (2005) was published in August 2005. Sichter (Hindemith), Bummelchen

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Osama Bin Laden’s strategy, which he described in famous videotape which was found in a hastily deserted house in Afghanistan. In the transcript (Department of Defense), Laden mentions that

Those who were trained to fly didn’t know the others. One group of people did not know the other group.

Usama bin Laden even described this strategy on his infamous video tape which was found in a hastily deserted house in Afghanistan. In the transcript (Department of Defense, 2001) bin Laden mentions:

Those who were trained to fly didn’t know the others. One group of people did not know the other group.

 Anmerkungen No reference given. Sichter (Graf Isolan), Hindemith

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In this case study we consider the connections network of terrorists involved in the Bali Night Club Bombing and their directed or undirected relationships with other entities. Of course mapping networks after an event is relatively easy, while the real problem in this case is to map the covert networks to prevent terrorist activity, a task that can be more difficult. As a second example we consider the connections network of the hijackers and related terrorists of the September 2001 attacks. Of course mapping networks after an event is relatively easy, while the real problem in this case is to map covert networks to prevent criminal activity, a task that can be much more difficult.
 Anmerkungen Nearly identical at the end but nothing is marked as a citation; the reference is not given. Sichter (Graf Isolan), Bummelchen

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Shukrijumah is believed to be working on Osama bin Laden’s plan to trigger a radiological disaster inside the United States – the so-called “dirty-bomb” scenario where a small charge would trigger dispersion of radiation over a large area, causing chaos on those caught in the blast and making the blast area uninhabitable. It is to mention that the high-grade uranium is not necessary for this project; ordinary, low-grade nuclear waste will be deadly enough. Adnan El Shukrijumah [...] He is believed to be working on Osama bin Laden’s plan to trigger a radiological disaster inside the United States – the so-called “dirty-bomb” scenario where a small charge would trigger dispersion of radiation over a large area, wreaking havoc on those caught in the blast and making the blast area uninhabitable.

 Anmerkungen The source is not named; the phrasing in the source Wikipedia given in footnote 27 5 lines earlier on the first mention of the name Shukrijumah does not match. Sichter (Graf Isolan), WiseWoman

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It is believed by authorities that Shukrijumah may have been trained at an Al Qaeda terrorist camp. Shukrijumah has extensive flight training that he received at a flight school in Florida and is a pilot, though he is not registered with the Federal Aviation Administration (FAA). Apparently, this is of concern to law enforcement since practically all the Al Qaeda hijackers involved in the September 11 terrorist attacks, received training to be pilots at U.S. private flight schools.

It is also believed by the FBI, that Shukrijumah has been trained by Al Qaeda to operate as a terrorist organizer and operational / field commander and lead or coordinate a terrorist assault, in much the [same way that Mohammed Atta was designated and trained as an organizer and operational/field leader by Al Qaeda to lead the September 11 hijackers in killing 3,100 by attacking The Pentagon in Arlington, Virginia and leveling the World Trade Center in Manhattan, New York City, New York.]

It is believed by authorities that Shukrijumah may had been trained at an al-Qaida terrorist camp. Shukrijumah has extensive flight training that he received at a flight school in Florida and is a pilot, though he is not registered with the Federal Aviation Administration (FAA). Obviously, this is of concern to law enforcement since virtually all the al-Qaida hijackers involved in the September 11 terrorist attacks, received training to be pilots at U.S. private flight schools. Also, it is believed by the FBI that Shukrijumah has been trained by al-Qaida to operate as a terrorist organizer and opeartional/field commander and lead or coordinate a terrorist assault, much the same way Mohammed Atta was designated and trained as an organizer and operational/field leader by al-Qaida to lead the September 11 hijackers in killing 3,100 by attacking The Pentagon in Arlington, Virginia and leveling the World Trade Center in Manhattan, New York City, New York.
 Anmerkungen Several pages earlier Nm named http://en.wikipedia.org/wiki/Adnan_el-Shukrijumah as the source for his information on Shukrijumah. He did not mention that further on one would find a nearly identical copy of the original text in this thesis. Sichter (Graf Isolan), WiseWoman

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 Anmerkungen continued from previous page Nm draws his information concerning the whereabouts of El Shukrijumah from Wikipedia only (without telling anybody). Sichter (Graf Isolan), Bummelchen

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5.4.3 WTC 1993 Bombing Plot[FN 28]

The WTC bombing attack in the garage occurred on February 26, 1993 of the New York World Trade Center. A car bomb was planted by terrorist groups in the underground parking garage below tower One. It is reported that the attack killed six, and injured over 1,000 and gave indication for the 9/11 attacks on the same buildings.

---

The World Trade Center bombing was the February 26, 1993, terrorist attack in the garage of the New York City World Trade Center. A car bomb was detonated by Arab Islamist terrorists in the underground parking garage below Tower One. It killed six, injured over 1,000, and presaged the September 11, 2001, terrorist attacks on the same buildings.
 Anmerkungen The original has only been slightly adapted. Large subsentences were left unchanged and would have required citation. Sichter (Graf Isolan), Bummelchen

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Ramzi Yousef began in 1991 to plan a bombing attack at USA. Yousef’s Uncle Khalid Shaikh Mohammed gave him advice and tips over the phone, and funded him. Yousef entered the USA with a false passport in 1992. Ramzi Yousef, born in Kuwait, began in 1991 to plan a bombing attack within the United States. Yousef's uncle Khalid Shaikh Mohammed, [...] gave him advice and tips over the phone, and funded him with a US\$660 wire transfer.[FN 1]

Yousef entered the United States with a false Iraqi passport in 1992.

 Anmerkungen slightly shortened but still extremely close to the original. The source is given on the previous page. Sichter (Graf Isolan), Bummelchen

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Oplan Bojinka was a planned large scale attack on airliners in 1995. It is reported that the term refers to “airline bombing plot” alone, or that combined with the “Pope assassination plot” and the “CIA plane crash plot”. The first (“airline bombing plot” ) refers to a plot to destroy 11 airliners on January 21 and 22, 1995, the second (“Pope assassination plot” ) refers to a plan to kill Pope John Paul II on January 15, 1995, and the third (“CIA plane crash plot”.) refers a plan to crash a plane into the CIA headquarters in Fairfax County, Virginia and other buildings. Oplan Bonjika was prevented on January 6, and 7, 1995, but some lessons learnt were apparently used by the planners of the September 11 attacks.

It is also reported that the money that funded operation Bojinka came from Osama Bin Laden and (R. Isamuddin) Hambali, and also from organizations operated by Jamal Khalifa, Bin Laden’s brother in law.