## FANDOM

33.373 Seiten

227 gesichtete, geschützte Fragmente: Plagiat

 Bearbeitet: 6. March 2014, 16:05 WiseWomanErstellt: 30. April 2012, 12:07 (Graf Isolan)

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The http://www.trackingthethreat.com/ is a knowledge base of open source information about the Al Qaeda terrorist network, developed as a research project of the FMS Advanced Systems Group. The main goal of this project 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. Our 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 A near literal copy of TrackingTheThreat.com self-description; nothing is marked as a citation. Sichter (Graf Isolan), Bummelchen

 Bearbeitet: 6. March 2014, 16:02 WiseWomanErstellt: 30. April 2012, 12:37 (Graf Isolan)

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This knowledge base represents the original open-source knowledge base on Al Qaeda. It contains data in the form of:

Entities: Discrete data elements that comprise people, places, organizations, events, etc.

Relationships: Information about the personal, organizational, transactional, and historical connections between entities.

Notes and Documents: Unstructured text that provides background information on entities, relationships, and metadata.

TrackingTheThreat.com is database of open-source information about the Al Qaeda terrorist network. It contains data in the form of:

&bullet; Entities: Discrete data elements that comprise people, places, organizations, events, etc.

&bullet; Relationships: Information about the personal, organizational, transactional, and historical connections between entities.

&bullet; Notes and Documents: Unstructured text that provides background information on entities, relationships, and metadata.

 Anmerkungen an almost literal copy, not marked as a citation Sichter (Graf Isolan), Bummelchen

 Bearbeitet: 6. March 2014, 16:02 WiseWomanErstellt: 30. April 2012, 12:21 (Graf Isolan)

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This site contains information collected from thousands of open source reports, documents, news stories, and other places which are deemed appropriate. It is presented in a concise and organized fashion as a demonstration of some of the capabilities of Sentinel TMS. The group have attempted to assign some degree of credibility to its accuracy, no representation is made or implied that all data contained herein is completely reliable. This site contains information collected from thousands of open source reports, documents, news stories, and other places which are deemed worthy of note. It is presented here in a concise and organized fashion as a demonstration of some of the capabilities of Sentinel TMS. While we have attempted to assign some degree of credibility to its accuracy, no representation is made or implied that all data contained herein is completely reliable.
 Anmerkungen A barely concealed copy of the original text. Sichter (Graf Isolan), Bummelchen

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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

 Bearbeitet: 6. March 2014, 15:33 WiseWomanErstellt: 14. April 2012, 18:48 (Graf Isolan)

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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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[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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[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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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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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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• Terrorist organizations such as Al Qaeda

• Terrorists such as Osama Bin Ladin, Ramzi Yousef, etc.

• Terrorist facilities such as Darunta Training Camp, Khalden Training Camp, etc.

• Terrorist events/attacks such as 9/11, WTC terrorist attack 2003, etc.

The dataset also contains various types of relations connecting instances of different entity types. Here is a partial list of the various relation types:

• memberOf: instances of terrorist can be affiliated with various instances of terrorist organization.

• facilityOwner: instances of terrorist facility are usually run by instances of terrorist organizations.

• facilityMember: instances of terrorist are linked to various instances of terrorist facilities if the terrorist instance attended/spent some time at the facility.

• claimResponsibility: instances of terrorist organization are linked to the instances of terror attacks they claim responsibility for.

• participatedIn: instances of terrorist that may have participated in instances of terror attacks.

Terrorist organizations such as Hamas, Hizballah, Liberation Tigers of Tamil Eelam (LTTE), etc.

Terrorists such as Osama bin Ladin, Ramzi Yousef, etc.

Terrorist facilities such as Darunta Training Camp, Khalden Training Camp, etc.

Terrorist events/attacks such as African embassy bombings of 1998, Madrid Bombings of 2004, etc.

[...]

The dataset also contains various types of relations connecting instances of different entity types. Here is a partial list of the various relation types:

memberOf: instances of terrorist can be affiliated with various instances of terrorist organization.

facilityOwner: instances of terrorist facility are usually run by instances of terrorist organizations.

facilityMember: instances of terrorist are linked to various instances of terrorist facilities if the terrorist instance attended/spent some time at the facility.

claimResponsibility: instances of terrorist organization are linked to the instances of terror attacks they claim responsibility for.

participate: instances of terrorist may participate in instances of terror attacks.

 Anmerkungen continues the verbatim take-over of the description of another research group's counter-terrorism knowledge base; Mark also the transcription "Bin Ladin" which corresponds to the one given in the source, but which is used nowhere else in Nm's thesis (Usually Nm writes "Bin Laden"). Sichter (Graf Isolan), fiesh

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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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In the wake of the information revolution, the interest in studying the network structure of organizations, in particular criminal in nature, has increased manifold. Social network concepts, regardless of their flexibility, have come to the forefront especially for these applications. This chapter introduces and studies cohesion analysis of terrorist networks.

Cohesion analysis is often used to explain and develop sociological theories. Members of a cohesive subgroup tend to share information, have homogeneity of thought, identity, beliefs, behaviour, even food habits and illnesses (Wasserman, S., Faust, K., 1994). Social cohesion is also believed to influence emergence of consensus among group members. Examples of cohesive subgroups include religious sects, terrorist groups, criminal gangs/ organized criminals, military teams, and tribal clusters, etc.

Some direct application areas of social networks include studying terrorist networks (Sageman, M., 2004; Berry, N. et al., 2004), as mentioned earlier chapters a special application of criminal network analysis. It is anticipated to study organized crimes for example, terrorism, drug trafficking and money laundering, (McAndrew, D.,1999:; Chen, H. et al, 2004)), identity theft, credit card crime, child pornography, human smuggling. It is worthy to mention that SNA concepts provide suitable data mining tools for this purpose (Davis, R.H, 1981).

In the wake of the information revolution, the interest in studying the network

structure of organizations, in particular criminal in nature, has increased manifold. Social network concepts, despite their versatility, have come to the forefront especially for these applications. [...]

[Page 2]

Social cohesion is often used to explain and develop sociological theories. Members of a cohesive subgroup tend to share information, have homogeneity of thought, identity, beliefs, behavior, even food habits and illnesses [52]. Social cohesion is also believed to influence emergence of consensus among group members. Examples of cohesive subgroups include religious cults, terrorist cells, criminal gangs, military platoons, sports teams and conferences, work groups etc.

[...]

Some direct application areas of social networks include studying terrorist networks [43,9], which is essentially a special application of criminal network analysis that is intended to study organized crimes such as terrorism, drug trafficking and money laundering [36,21]. Concepts of social network analysis provide suitable data mining tools for this purpose [17].

[9]. Berry, N., Ko, T., Moy, T., Smrcka, J., Turnley, J., Wu, B.:[...] (2004). [...]

[17]. Chen, H., Chung, W., Xu, J.J., Wang, G., Qin, Y., Chau, M.: [...] (2004)

[21]. Davis, R.H.:[...] (1981)

[36]. McAndrew, D.: [...] (1999)

[43]. Sageman, M.: [...] (2004)

[52]. Wasserman, S., Faust, K.: [...] (1994)

 Anmerkungen The source is not given. The text is copied including all literature references. Only minor adjustments have been made. Sichter (Hindemith), Graf Isolan

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It is reported that mathematically modeling a cohesive subgroup has been a subject of interest in social network analysis since many decades.

As stated earlier, one of the earliest graph models used for studying cohesive subgroups was the clique model (Luce, R., Perry, A., 1949). A clique is a subgraph in which there is an edge between any two nodes. However, the clique approach has been criticized for its [restrictive nature.]

Modeling a cohesive subgroup mathematically has long been a subject of interest in social network analysis. One of the earliest graph models used for studying cohesive subgroups was the clique model [35]. A clique is a subgraph in which there is an edge between any two vertices. However, the clique approach has been criticized for its overly restrictive nature [...]

[35]. Luce, R., Perry, A.: [...] (1949)

 Anmerkungen To be continued on the next page: Nm/Fragment_222_01 Sichter (Hindemith), Graf Isolan

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[However, the clique approach has been criticized for its] restrictive nature. More details can be found in (Scott, J., 2000; Wasserman, S., Faust, K., 1994) and modeling disadvantages (Seidman, S.B., Foster, B.L., 1978; Freeman, L.C., 1992).

It is important to note that clique models provide three important structural properties that are expected of a cohesive subgroup, namely:

1. familiarity (each node has many neighbours and only a few strangers in the group),

2. reachability (a low diameter, facilitating fast communication between the group members) and

3. robustness (high connectivity, making it difficult to destroy the group by removing members).

As mentioned earlier, different models relax different aspects of a cohesive subgroup. In this context, Luce R. introduced a distance based model known as n-clique (Luce, R., 1950). This model was also studied along with a variant called n-clan by Mokken (1979).

Some drawbacks are pointed out and the models are appropriately redefined in (Balasundaram, B., Butenko, S., Trukhanov, S., 2005). All these models highlight the need for high reachability inside a cohesive subgroup and have their own advantages and disadvantages as models of cohesiveness.

The other variation is degree based model which is known as k-plex (Wasserman, S., Faust, K., 1994). This model relaxes familiarity within a cohesive subgroup and implicitly provides reachability and robustness.

However, the clique approach has been criticized for its overly restrictive nature [2,52] and modeling disadvantages [47,25].

[...] Clique models idealize three important structural properties that are expected of a cohesive subgroup, namely, familiarity (each vertex has many neighbors and only a few strangers in the group), reachability (a low diameter, facilitating fast communication between the group members) and robustness (high connectivity, making it difficult to destroy the group by removing members). Different models relax different aspects of a cohesive subgroup. [34] introduced a distance based model called k-clique [...]. These models were also studied along with a variant called k-clan by Mokken [38]. [...] These drawbacks are pointed out and the models are appropriately redefined in [7], as described in Section 2. All these models emphasize the need for high reachability inside a cohesive subgroup and have their own merits and demerits as models of cohesiveness. The focus of this paper is on a degree based model introduced in [47] and called k-plex. This model relaxes familiarity within a cohesive subgroup and implicitly provides reachability and robustness.

[2]. Alba, R.: [...] (1973)

[7]. Balasundaram, B., Butenko, S., Trukhanov, S.:[...] (2005)

[25]. Freeman, L.C.: [...] (1992)

[34]. Luce, R.:[...] (1950)

[38]. Mokken, R.: [...] (1979)

[47]. Seidman, S.B., Foster, B.L.: [...] (1978)

[52]. Wasserman, S., Faust, K.: [...] (1994)

 Anmerkungen The text is copied from the source, which is not given anywhere in the thesis. Some adaptations have taken place, which sometimes did not result in a coherent text (see first paragraph of this fragment). Sichter (Hindemith), Graf Isolan

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Further real-time or near real-time information from a multiplicity of databases could have the potential to generate early warning signals of utility in detecting and deterring terrorist attacks. It is necessary, of course, to have experts in the loop. It has not escaped our notice that SNA, duly validated and used with real-time or near real-time information from a multiplicity of databases could have the potential to generate early warning signals of utility in detecting and deterring terrorist attacks. It is necessary, of course, to have ‘experts’ in the loop.
 Anmerkungen Some conclusion is also taken from Saxena et al. 2004 Sichter (Hindemith), 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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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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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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In the aftermath of the September 11th attacks, it was noted that comprehensible information sources were not available to the researchers (Sageman 2004). Information was either available in a disconnected form, not allowing for comparison studies across events, groups or tactics, or made available in written articles – which are not readily suitable for quantitative analysis of terrorist networks. Data collected by law enforcement agencies, while potentially better organized, are largely not available to the research community due to restrictions in distribution of sensitive information. In the aftermath of the September 11th attacks, it was noted that coherent information sources on terrorism and terrorist groups were not available to researchers[10]. Information was either available in fragmentary form, not allowing comparison studies across incidents, groups or tactics, or made available in written articles - which are not readily suitable for quantitative analysis of terrorist networks. Data collected by intelligence and law-enforcement agencies, while potentially better organized, is largely not available to the research community due to restrictions in distribution of sensitive information.

[10] Le Gruenwald, Gary McNutt, and Adrien Mercier. Using an ontology to improve search in a terrorism database system. Proceedings of the 14th International Workshop on Database and Expert System Applications (DEXA'03), 2003.

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

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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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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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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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The iMiner knowledge base (see Figure 7.5) consists of various types of entities. Here is an incomplete list of the different entity types: The PIT knowledge base consists of various types of entities. Here is an incomplete list of the different entity types:
 Anmerkungen This is just the start of the nearly word-for-word copy of descriptions and names for entities and relation types of a knowledge base for "counter-terrorism research". This begs the question if Nm's iMiner knowledge base - which is supposed to be the scientific core of his thesis - is as original as it should be or if it is also just a copy - a copy of the PIT knowledge base which is described in the unnamed source. Sichter (Graf Isolan), Hindemith

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The analysis of relational data is a rapidly growing area within the larger research community interested in machine learning, knowledge discovery, and data mining. Several workshops [(Dzeroski, S., De Raedt, L., and Wrobel, S. 2002; Getoor, L., and Jensen, D. 2000; Jensen, D. and Goldberg, H. 1998) have focused on this particular topic, and another DARPA research program — Evidence Extraction and Link Discovery (EELD) —focuses on extracting, representing, reasoning with, and learning from relational data.] Analysis of relational data is a rapidly growing area within the larger research community interested in machine learning, knowledge discovery, and data mining. Several recent workshops [EN 3, EN 6, EN 8] have focused on this precise topic, and another DARPA research program — Evidence Extraction and Link Discovery (EELD) —focuses on extracting, representing, reasoning with, and learning from relational data. [FN 5]

---

[EN 3] Dzeroski, S., De Raedt, L., and Wrobel, S. (Eds). Papers of the Workshop on Multi-Relational Data Mining. The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, 2002.

[EN 6] Getoor, L., and Jensen, D. (Eds). Learning Statistical Models from Relational Data: Papers from the AAAI 2000 Workshop, AAAI Press, Menlo Park CA, 2000.

[EN 8] Jensen, D. and Goldberg, H. Artificial Intelligence and Link Analysis: Papers from the 1998 AAAI Fall Symposium., AAAI Press, Menlo Park CA, 1998.

 Anmerkungen Verbatim with the references as in the source, which is not referenced, although it represents another workshop of those described. Sichter (Graf Isolan), Hindemith

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[The analysis of relational data is a rapidly growing area within the larger research community interested in machine learning, knowledge discovery, and data mining. Several workshops] (Dzeroski, S., De Raedt, L., and Wrobel, S. 2002; Getoor, L., and Jensen, D. 2000; Jensen, D. and Goldberg, H. 1998) have focused on this particular topic, and another DARPA research program — Evidence Extraction and Link Discovery (EELD) —focuses on extracting, representing, reasoning with, and learning from relational data. Analysis of relational data is a rapidly growing area within the larger research community interested in machine learning, knowledge discovery, and data mining. Several recent workshops [EN 3, EN 6, EN 8] have focused on this precise topic, and another DARPA research program — Evidence Extraction and Link Discovery (EELD) —focuses on extracting, representing, reasoning with, and learning from relational data. [FN 5]

---

[EN 3] Dzeroski, S., De Raedt, L., and Wrobel, S. (Eds). Papers of the Workshop on Multi-Relational Data Mining. The Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, 2002.

[EN 6] Getoor, L., and Jensen, D. (Eds). Learning Statistical Models from Relational Data: Papers from the AAAI 2000 Workshop, AAAI Press, Menlo Park CA, 2000.

[EN 8] Jensen, D. and Goldberg, H. Artificial Intelligence and Link Analysis: Papers from the 1998 AAAI Fall Symposium., AAAI Press, Menlo Park CA, 1998.

 Anmerkungen continued from previous page 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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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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 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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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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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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It is 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. 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 Nearly identical, not marked as a citation, no reference given. See also Nm/Fragment_144_16. 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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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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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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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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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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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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“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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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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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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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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[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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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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(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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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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[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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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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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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[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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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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[Social network analysis in general studies behaviour of the individuals at the micro level, the pattern of relationships (network] structure) at the macro level, and the interactions between the two.

The analysis of the communication structures that is comprised in SNA study is known as an important element in the analysis of the micro-macro link, the way in which individual behaviour and social phenomena are linked with one another. In this sense, social networks can assist the analysts both the root cause and the result of the behaviour of an individual.

It is fact that social networks study provides and bound chances of individual selections in the mean-time time individuals initiate, build, continue, and break up links and by doing so define the universal structure of a network. However, network structure is seldom constructed by its individuals. It is known as the ‘unintended’ effect of the actions of the individual and can as be called a “spontaneous order”.

Social network analysis [FN 1] studies the behavior of the individual [FN 2] at the micro level, the pattern of relationships (network structure) at the macro level, and the interactions between the two. The analysis of the interaction structures that is involved in social network analysis is an important element in the analysis of the macro-micro-macro link, the way in which individual behavior and collective phenomena are connected with one another. In this perspective, social networks are both the cause of and the result of individual behavior. Social networks provide and limit opportunities of individual choices, whereas at the same time individuals initiate, construct, maintain, and break up relationships and by so doing determine the global structure of the network. However, individuals seldom consciously construct network structures beyond their own relationships. The overall network structures are often the ‘unintended’ effect of individual actions and can as such be called a “spontaneous order” (see e.g. Hayek 1973).
 Anmerkungen No reference given. The content is identical, but formulations have been somewhat adapted. Sichter (Hindemith), Bummelchen

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Social network analysis in general studies behaviour of the individuals at the micro level, the pattern of relationships (network [structure) at the macro level, and the interactions between the two.] Social network analysis [FN 1] studies the behavior of the individual [FN 2] at the micro level, the pattern of relationships (network structure) at the macro level, and the interactions between the two.
 Anmerkungen To be continued on the next page. The source is not mentioned in the thesis at all. Sichter (Hindemith), 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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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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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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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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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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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.

Oplan Bojinka [...] was a planned large-scale attack on airliners in 1995, [...].

The term can refer to the "airline bombing plot" alone, or that combined with the "Pope assassination plot" and the "CIA plane crash plot". The first refers to a plot to destroy 11 airliners on January 21 and 22, 1995, the second refers to a plan to kill Pope John Paul II on January 15, 1995, and the third refers a plan to crash a plane into the CIA headquarters in Fairfax County, Virginia and other buildings. Oplan Bojinka was prevented on January 6 and 7, 1995, but some lessons learned were apparently used by the planners of the September 11 attacks.

[...]

The money that funded Operation Bojinka came from Osama bin Laden and Hambali, and from front organizations operated by Mohammed Jamal Khalifa, bin Laden's brother-in-law.

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

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[The models presented in this dissertation offer an] investigative data mining tool that lends itself to data-sparse environment initially confronted by law enforcement and intelligence agencies. The methodology offers a screening tool that lends itself to the data-sparse environment initially confronted by analysts.
 Anmerkungen this is the end of Nm/Fragment_240_24 Sichter (Graf Isolan), Bummelchen

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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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&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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We assume that limited information that captured the dyadic interactions between terrorists was available. The models presented in this dissertation offer an [investigative data mining tool that lends itself to data-sparse environment initially confronted by law enforcement and intelligence agencies.] The study begins with the assumption that limited information that captured

the dyadic interactions between individuals was available. The methodology offers a screening tool that lends itself to the data-sparse environment initially confronted by analysts.

 Anmerkungen The finding is quite short and has been adapted, but still, some text clearly has been copied without proper attribution. Sichter (Hindemith) Agrippina1

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Considering the nature of this war, understanding the structure of these networks is paramount. No longer fitting the traditional paradigm of combat between great armies, this war involves not only defeating the individuals actively threatening National Security, but also alleviating the environments that nurture the [development and continuity of such groups.] Considering the nature of this war, understanding the enemy is paramount. No longer fitting the traditional paradigm of combat between great armies, this war involves not only defeating the individuals actively threatening our National Security, but also mitigating the environments that nurture the development and continuity of such groups.
 Anmerkungen Even the final summary is copied from somewhere else. Sichter (Hindemith) Agrippina1

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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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[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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Knowledge about the structure and organization of terrorist networks is important for both terrorism investigation and the

development of effective strategies to prevent terrorist attacks. However, except for network visualization, terrorist network analysis remains primarily a manual process. Existing tools do not provide advanced structural analysis techniques that allow for the extraction of network knowledge from terrorist organizations data. To help law enforcement and intelligence agencies discover terrorist network knowledge efficiently and effectively, in this research we propose a framework for automated network analysis, visualization and destabilization.

Based upon this framework, this project developed a system called iMiner that incorporates several advanced techniques: subgroups detection approach, discovering efficiency of a network, social network analysis methods, destabilizing strategies for terrorist networks including detection of hidden hierarchy.

Knowledge about the structure and organization of criminal networks is important for both crime investigation and the development of effective strategies to prevent crimes. However, except for network visualization, criminal network analysis remains primarily a manual process. Existing tools do not provide advanced structural analysis techniques that allow extraction of network knowledge from large volumes of criminal-justice data. To help law enforcement and intelligence agencies discover criminal network knowledge efficiently and effectively, in this research we proposed a framework for automated network analysis and visualization. The framework included four stages: network creation, network partition, structural analysis, and network visualization. Based upon it, we have developed a system called CrimeNet Explorer that incorporates several advanced techniques: a concept space approach, hierarchical clustering, social network analysis methods, and multidimensional scaling.
 Anmerkungen This is just the introduction from the major paper of Xu and Chen (2005b), in which they present their "CrimeNet Explorer" system, slightly rewritten to fit terrorist networks instead of criminal networks. The source is not named here. Sichter (Graf Isolan), Bummelchen

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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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In order to accomplish this, analysts must at minimum (1) improve the understanding why people would undertake such activities and mentally prepare to be used as human bombs; (2) identify vulnerabilities existing within these networks and how to exploit them; and, (3) determine what consequences may follow an operation to minimize the likelihood that actions executed unintentionally contribute to the environments that promote extremism. In order to accomplish this, the appropriate communities must at a minimum

(1) improve the understanding of why people would undertake such activities; (2) identify limitations or vulnerabilities existing within non-cooperative networks and how to exploit them; and, (3) determine what repercussions may follow an operation to minimize the likelihood that actions executed inadvertently contribute to the environments that promote extremism.

 Anmerkungen Not even the last words of the thesis are from the author Sichter (Hindemith) Agrippina1

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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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 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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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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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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These measures could be useful for law enforcement and intelligence agencies to disrupt the effective operation and growth of these networks or destroy some terrorist cells entirely. Although these adversaries can be affected in a number of ways, this research focuses upon capturing / eradicating a terrorist organization’s most influential persons or finding susceptible points of entry and conveying information or influence that contribute to winning the war against terrorism. This chapter provides a summary of methodological and practical contributions of this dissertation as well as recommendations for areas of future research. Such opportunities lie within the ability to disrupt the effective operation and

growth of these networks, or destroy them entirely. Although these adversaries can be affected in a number of ways, this research focuses upon either removing or mitigating an organization’s most influential individuals, or finding susceptible points of entry and conveying information or influence that contributes to winning this war of ideas. This chapter provides a summary of the methodological and practical contributions of this dissertation, as well as recommendations for areas of future research.

 Anmerkungen Even in the overview of the conclusions of the thesis there is copied text without referencing the original author. Sichter (Hindemith) Agrippina1

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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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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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6.4.1.3 Finding connections between groups of objects

Consider the following witness statement, “I saw Alam and Memon in a car with two other men. If Alam and Memon were two suspects in a terrorist plot then the police would like to identify the two other men. Support for finding connections between groups of objects is provided by this facility.

Consider the witness statement above, using this facility to retrieve objects that are instances of the person object class and connected to the objects Victoria Garage, Saeen, London Gym, and Donald as shown in Figure 6.6. Thus, Saeen and Donald are identified as possible companions of Alam and Memon.

5.2.5 Finding connections between groups of objects

Consider the following witness statement, “I saw Alan and Mike drinking in the King George public house with two other men. They left in a van with Kelly Building Contractors written on the side”. If Alan and Mike were two suspects in an armed robbery then the police would want to identify the two other men. Support for finding connections between groups of objects is provided through the centre facility.

[...]

Consider the witness statement above. Using the centre facility to retrieve objects that are instances of the person object class and connected to the objects Mike, Alan, the King George, and the Kelly Building firm by a path of length three or less would result in the objects and links shown in figure 5.5. Thus Joe, Richard and Ronald are identified as possible drinking companions, [...]

 Anmerkungen continued from previous page adapted to "terrorism research" and using a slightly changed example, otherwise nearly identical Sichter (Graf Isolan), Hindemith

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6.4.1.2 Finding paths that connect two specified objects

If Saeen was to be charged with participating in a bombing plot and the police suspected that Mars was also involved, the investigative officer(s) would be likely to want to know if, and how, Mars and Saeen are connected. Paths in the database connecting two specified entities already on the browsing canvas may be displayed using all path facility as shown in Figure 6.5.

[Text accompanying Figure 6.5]

Figure 6.5. The using all path facility to display all those paths in the database that is displayed in Fig. 2. that connect the objects representing Saeen and Mars.

5.2.4 Finding paths that connect two specified objects

If Sandra was to be charged with participating in an armed robbery and the police suspected that Mary was also involved, the investigating officer(s) would be likely to want to know if, and how, Mary and Sandra are connected. Paths in the database connecting two specified objects already on the browsing canvas may be displayed using either the path facility, or the structured path facility.

[Text accompanying Figure 5.3]

Figure 5.3: The result of using the path facility to display all those paths in the database that is displayed in figure 5.1 of length four or less that connect the objects representing Sandra and Mary.

 Anmerkungen continued from previous page. Note how Nm adapts the text to make it suitable for the terrorithm theme of the thesis: "Mary" becomes "Mars", "Sandra" becomes "Saeen" and an "armed robbery" becomes a "bombing plot". Sichter (Graf Isolan), Hindemith

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6.4.1.1 Retrieving all objects that are directly or indirectly connected to a specified object

This facility may be used to retrieve and display all objects stored in the database that are connected an object already displayed on the browsing canvas by a path in the database of an arbitrary length, the associated connecting paths also being displayed. A user may specify the maximum length of a path from the object and the type of links that a connecting path may include.

For example, if an object representing Saeen was placed on the browsing canvas and this facility was used to display all objects connected to Saeen by a path of length two or less then the objects and links shown in Figure 6.4 would be displayed.

5.2.3 Retrieving all objects that are directly or indirectly connected to a specified object

The vicinity facility may be used to retrieve and display all objects stored in the database that are connected to an object already displayed on the browsing canvas by a path in the database of an arbitrary length, the associated connecting paths also being displayed. The user may specify the maximum length of a path from the object and the type of links that connecting paths may include.

For example, if the object representing Sandra was placed on the browsing canvas and the vicinity facility was used to display all objects connected to Sandra by a path of length two or less then the objects and links shown in figure 5.2 would be displayed.

 Anmerkungen continued from previous page Sichter (Graf Isolan), Hindemith

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The iMiner is an experimental system, which provides facilities for retrieval of information and its presentation in graph form. A number of facilities that enable small subgraphs to be retrieved and added to the browsing canvas are also provided. In the current implementation we have provided four such facilities that are described in next section.

6.4.1 The Subgraph Retrieval Facilities

An analyst begins to construct a view by placing one or more objects on the browsing canvas, either by selecting from the list of those stored, or by retrieving objects according to their attribute values. The user may then begin to use the facilities that we now describe using the example of the database shown in Figure. 6.3.

The Exploratory Database View Constructor (EDVC) is an experimental system, which provides facilities for the incremental exploratory retrieval of information and its presentation in graph form as described in section 2 above. A number of facilities that enable small sub-graphs to be retrieved and added to the browsing canvas [FN 13] are also provided. In the current implementation we have provided seven such facilities that are described in the next section.

5.2 The sub-graph retrieval facilities

5.2.1 Introduction

The user begins to construct a view by placing one or more objects on the browsing canvas, either by selecting from a list of those stored, or by retrieving objects according to their attribute values [FN 14]. The user may then begin to use the facilities that we now describe using the example database shown in figure 5.1 [FN 15].

---

[FN 13] We use the term “browsing canvas” throughout this section to refer to the display area on which the database view or chart is constructed.

[FN 14] Objects are retrieved in this manner by specifying conditions that the attribute values of the objects retrieved must satisfy. These conditions are specified in an attribute condition grid similar to that used in QBE [ZLOO75].

[FN 15] The criminal intelligence database depicted in figure 5.1 and the examples presented throughout this section are based upon those presented in [AYRE95].

 Anmerkungen It has already been officially noted in 2009 (see [1]) that Nm has taken parts of this source for yet another of his papers with "insufficient attribution (including appropriate references to the original author(s) and/or paper titles) and without permission". Here the source is not given, either. Sichter (Graf Isolan), Hindemith

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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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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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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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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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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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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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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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[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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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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The first stage of network analysis development is to automatically identify the [strongest association paths, or geodesics, between two or more network members.] The first stage of our network analysis development was intended to automatically identify the strongest association paths, or geodesics, between two or more network members using shortest-path algorithms.
 Anmerkungen reference is never named Sichter (Graf Isolan), Bummelchen

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[The first stage of network analysis development is to automatically identify the] strongest association paths, or geodesics, between two or more network members. In practice, such a task often entails intelligence officials manually exploring links and trying to find association paths that might be useful for generating investigative leads. The first stage of our network analysis development was intended to automatically identify the strongest association paths, or geodesics, between two or more network members using shortest-path algorithms. In practice, such a task often entails crime analysts to manually explore links and try to find association paths that might be useful for generating investigative leads.
 Anmerkungen no reference given Sichter (Graf Isolan), Bummelchen

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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 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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In this Section we discussed Prefuse (which we have used a tool for visualization purpose of this study), a user interface toolkit for crafting interactive visualizations of structured and unstructured data. The Prefuse supports the design of 2D visualizations of any data consisting of discrete data entities, such as graphs, trees, scatter plots, collections, and timelines. The Prefuse implements existing theoretical models of information visualization to provide a flexible framework for simplifying application design and enabling reuse and composition of visualization and interaction techniques. In particular, Prefuse contributes scalable abstractions for filtering abstract data into visual content and using lists of composable actions to manipulate data in aggregate.

The prototype constructed in the research study using the Prefuse toolkit demonstrates the flexibility and performance of the Prefuse architecture.

In this paper we have introduced prefuse, a user interface toolkit for crafting interactive visualizations of structured and unstructured data. prefuse supports the design of 2D visualizations of any data consisting of discrete data entities, such as graphs, trees, scatter plots, collections, and timelines. prefuse implements existing theoretical models of information visualization to provide a flexible framework for simplifying application design and enabling reuse and composition of visualization and interaction techniques. In particular, prefuse contributes scalable abstractions for filtering abstract data into visual content and using lists of composable actions to manipulate data in aggregate.

Applications built with the toolkit demonstrate the flexibility and performance of the prefuse architecture.

 Anmerkungen continued from above Sichter (Graf Isolan), Bummelchen

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Colour Maps. [...] These maps can be configured directly, or automatically generated by analyzing attribute values.

Integrated Search. Prefuse also supports efficient keyword search in large data sets. This component builds a tree (Prefix tree) of requested data attributes, enabling searches that run in time proportional to the size of the query string. Search results matching a given query are then available for visualization as a FocusSet in the ItemRegistry’s FocusManager.

Event Logging. Prefuse also provides facilities for logging. It is bundled with an event logger for monitoring and recording both user interfacing events and internal events. These Recorded logs can also be used to review or replay a session. Prefuse also has synchronized the event logger with the output of an eye-tracker, enabling playback sessions annotated with subjects’ fixation points.

Color Maps. [...] These maps can be configured directly, [...] or automatically generated by analyzing attribute values.

Integrated Search. [...] the toolkit includes a FocusSet implementation to support efficient keyword search of large data sets. This component builds a trie (prefix tree) of requested data attributes, enabling searches that run in time proportional to the size of the query string. Search results matching a given query are then available for visualization as a FocusSet in the ItemRegistry’s FocusManager.

Event Logging. prefuse includes an event logger for monitoring and recording events. This includes both user interface events (mouse movement, focus selection) and internal system events (addition and deletion of items from the registry). [...] Recorded logs can be used to review or replay a session. We have even synchronized the event logger with the output of an eye-tracker, enabling us to playback sessions annotated with subjects’ fixation points.

 Anmerkungen continued from previous page Also see Nm/Fragment 183 25 where the source is mentioned. Sichter (Graf Isolan), Bummelchen

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[Available layouts include random, circular, gridbased, forcedirected, top-down (Reingold, E.M. and J.S. Tilford, 1981), radial (Yee, K.-P., D. Fisher, R. Dhamija, and M.A. Hearst; 2001),] indented outline, and tree map32 (Bruls, M., K. Huizing, and J.J. van Wijk, 2000) algorithms. These layouts are parameterized and reusable components. These facilitate the user to define their own new layouts by using existing modules. In addition, Prefuse supports space distortion of item location and size attributes, including graphical fisheye views (Sarkar, M. and M.H. Brown, 1992) and bifocal distortion (Leung, Y.K. and M.D. Apperley, 1992).

Force Simulation. Prefuse includes an extensible and configurable library for force-based physics simulations. This consists of a set of force functions, including n-body forces like gravity, spring forces, and drag forces. To support real-time interaction, n-body force calculations use the Barnes-Hut algorithm (Barnes, J. and P. Hut, 1986) to compute the otherwise quadratic calculation in log-linear time. The force simulation supports various numerical integration schemes. It is based on dynamic calculation of trade-offs in efficiency and accuracy, to update velocity and position values. These modules are based on numerical techniques like classic Runge-Kutta method. Again the design is flexible enough to accommodate the user defined extension to existing force based simulations.

Interactive Controls. Following the basic design of the Interactor paradigm (Myers, B.A., 1990), Prefuse includes parameterizable ControlListener instances for common interactions. It includes drag controls for repositioning ViualItems, focus controls for updating focus, navigation controls for panning and zooming, including both manual controls and speed-dependent automatic zooming (Igarashi, T. and K., 2000) and highlight settings in response to mouse actions and key press actions.

Available layouts include random, circular, gridbased, force-directed, top-down [EN 40], radial [EN 48], indented outline, and tree map [EN 10, EN 44] algorithms. These layouts are parameterized and reusable, hence one can write new layouts by composing existing modules. In addition, prefuse supports space distortion of item location and size attributes, including graphical fisheye views [EN 43] and bifocal distortion [EN 32].

Force Simulation. prefuse includes an extensible and configurable library for force-based physics simulations. This consists of a set of force functions, including n-body forces (e.g., gravity), spring forces, and drag forces. To support realtime interaction, n-body force calculations use the Barnes-Hut algorithm [EN 2] to compute the otherwise quadratic calculation in log-linear time. The force simulation supports various numerical integration schemes, with trade-offs in efficiency and accuracy, to update velocity and position values. The provided modules abstract the mathematical details of these techniques (e.g., 4th Order Runge-Kutta) from toolkit users. Users can also write custom force functions and add them to the simulator.

Interactive Controls. Inspired by the Interactor paradigm [EN 36], prefuse includes parameterizable ControlListener instances for common interactions. Provided controls include drag controls for repositioning items (or groups of items), focus controls for updating focus and highlight settings in response to mouse actions, and navigation controls for panning and zooming, including both manual controls and speeddependent automatic zooming [EN 25].

[EN 40] Reingold, E.M. and J.S. Tilford, Tidier Drawings of Trees. IEEE Transactions of Software Engineering, 1981. SE-7: p. 21-28.

[EN 48] Yee, K.-P., D. Fisher, R. Dhamija, and M.A. Hearst. Animated Exploration of Dynamic Graphs with Radial Layout. InfoVis'01. pp. 43-50 2001.

[EN 10] Bruls, M., K. Huizing, and J.J. van Wijk. Squarified TreeMaps. In Proceedings of Joint Eurographics and IEEE TCVG Symp. on Visualization (TCVG 2000): IEEE Press. pp. 33-42, 2000.

[EN 44] Treemaps for Space-Constrained Visualization of Hierarchies. 1998. http://www.cs.umd.edu/hcil/treemap-history/

[EN 43] Sarkar, M. and M.H. Brown. Graphical Fisheye Views of Graphs. CHI’92. pp. 83-91, May 1992.

[EN 32] Leung, Y.K. and M.D. Apperley, A Review and Taxonomy of Distortion-Oriented Presentation Techniques. ACM Transactions on Computer-Human Interaction, 1994. 1(2): p. 126-160.

[EN 2] Barnes, J. and P. Hut, A Hierarchical O(N Log N) Force Calculation Algorithm. Nature, 1986. 324(4).

[EN 36] Myers, B.A., A New Model for Handling Input. ACM Transactions on Information Systems, 1990. 8(3): p. 289-320.

[EN 25] Igarashi, T. and K. Hinckley. Speed-Dependent Automatic Zooming for Browsing Large Documents. UIST’00. pp. 139-148, 2000.

 Anmerkungen continued from previous page. Also see Nm/Fragment 183 25 where the source is mentioned. Sichter (Graf Isolan), Bummelchen

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[It also allows visual appearances to be easily changed, either by issuing different Renderers in response] to data attributes, or by changing the RendererFactory for a given ItemRegistry. This also provides a clean mechanism for semantic zooming (Perlin, K. and D. Fox, 1993) – the RendererFactory can select Renderers appropriate for the current scale value of a given display.

Display component presents the visualized data. Display acts as a camera onto the contents of an ItemRegistry. The Display is an extension of JComponent (Swing’s base component), and thus can be used in any Java Swing application. The Display takes an ordered enumeration of visible items from the registry, applies view transformations, computes the clipping region, and draws all visible items using appropriate Renderers. The Java2D library is used to support affine transformations of the view, including panning and zooming and other animation strategies. ItemRegistry can be tied to multiple Displays, enabling multiple views. Displays support interaction with visualized items through a ControlListener interface, providing callbacks in response to mouse and keyboard events on items. Displays also provide direct manipulation textediting of item content and allow arbitrary Swing components to be used as interactive tooltips.

6.3.7 The Prefuse Library

Prefuse architecture is supported by a huge library, a bundle of default implementations and significant components. These components simplify application design by providing advanced functions frequently used in visualizations.

Layout and Distortion. Prefuse library contains a variety of implemented actions to manage layout and distortion techniques. Available layouts include random, circular, gridbased, forcedirected, top-down (Reingold, E.M. and J.S. Tilford, 1981), radial (Yee, K.-P., D. Fisher, R. Dhamija, and M.A. Hearst; 2001), [indented outline, and tree map [EN 32] (Bruls, M., K. Huizing, and J.J. van Wijk, 2000) algorithms.]

It also allows visual appearances to be easily changed, either by issuing different Renderers in response to data attributes, or by changing the RendererFactory for a given ItemRegistry. This also provides a clean mechanism for semantic zooming [EN 38] – the RendererFactory can select Renderers appropriate for the current scale value of a given Display.

Presentation of visualized data is performed by a Display component, which acts as a camera onto the contents of an ItemRegistry. The Display subclasses Swing’s top-level JComponent, and can be used in any Java Swing application. The Display takes an ordered enumeration of visible items from the registry, applies view transformations, computes the clipping region, and draws all visible items using appropriate Renderers. The Java2D library is used to support affine transformations of the view, including panning and zooming. In addition, an ItemRegistry can be tied to multiple Displays, enabling multiple views (e.g., overview+detail [EN 12]).

Displays support interaction with visualized items through a ControlListener interface, providing callbacks in response to mouse and keyboard events on items. Displays also provide direct manipulation text-editing of item content and allow arbitrary Swing components to be used as interactive tooltips.

The prefuse Library

The core prefuse architecture described above is leveraged by a library of significant components. These components simplify application design by providing advanced functions frequently used in visualizations.

Layout and Distortion. prefuse is bundled with a library of Action modules, including a host of layout and distortion techniques. Available layouts include random, circular, gridbased, force-directed, top-down [EN 40], radial [EN 48], indented outline, and tree map [EN 10, EN 44] algorithms.

[EN 38] Perlin, K. and D. Fox. Pad: An Alternative Approach to the Computer Interface. SIGGRAPH'93. pp. 57-64, 1993.

[EN 12] Card, S.K., J.D. Mackinlay, and B. Shneiderman, Readings in Information Visualization: Using Vision to Think. San Francisco, California: Morgan-Kaufmann, 1999.

[EN 40] Reingold, E.M. and J.S. Tilford, Tidier Drawings of Trees. IEEE Transactions of Software Engineering, 1981. SE-7: p. 21-28.

[EN 48] Yee, K.-P., D. Fisher, R. Dhamija, and M.A. Hearst. Animated Exploration of Dynamic Graphs with Radial Layout. InfoVis'01. pp. 43-50 2001.

[EN 10] Bruls, M., K. Huizing, and J.J. van Wijk. Squarified TreeMaps. In Proceedings of Joint Eurographics and IEEE TCVG Symp. on Visualization (TCVG 2000): IEEE Press. pp. 33-42, 2000.

[EN 44] Treemaps for Space-Constrained Visualization of Hierarchies. 1998. http://www.cs.umd.edu/hcil/treemap-history/

 Anmerkungen continued from previous page. Also see Nm/Fragment 183 25 where the source is mentioned. Sichter (Graf Isolan), Bummelchen

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6.3 DESIGN OF THE PREFUSE TOOLKIT [FN 31]

In this Section the toolkit design (illustrated in Figure 6.2), presenting the architecture, basic abstractions, and provided libraries for processing and visualizing information is discussed.

[FN 31] The matter is taken from (Heer et al., 2005)

DESIGN OF THE PREFUSE TOOLKIT

We now describe the toolkit design (illustrated in Figure 2), presenting the architecture, basic abstractions, and provided libraries for processing and visualizing information.

 Anmerkungen Footnote 31 - indeed. In fact, already starting with the previous paragraph, there can be found nearly identical copies of large bodies of the original paper. This continues on the following pages of Nm's thesis. However, Heer et al. is not listed in the bibliography. Sichter (Graf Isolan), Bummelchen

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Although information visualization (infovis) techniques prove to be vital tools for making sense of complex data. To report these issues, we have used Prefuse, a software framework for creating dynamic visualizations of both structured and unstructured data. The Prefuse provides theoretically-motivated abstractions for the design of a wide range of visualization applications, enabling programmers to string together desired components quickly to create and customize working visualizations. Although information visualization (infovis) technologies have proven indispensable tools for making sense of complex data, wide-spread deployment has yet to take hold, [...] To address these issues, we have created prefuse, a software framework for creating dynamic visualizations of both structured and unstructured data. prefuse provides theoretically-motivated abstractions for the design of a wide range of visualization applications, enabling programmers to string together desired components quickly to create and customize working visualizations.
 Anmerkungen The source will be given later on with regard to the following paragraphs. Sichter (Graf Isolan), Bummelchen

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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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6.3.2 Filtering

Filtering is the process of mapping abstract data to a representation suitable for visualization. [...] The corresponding visual analogues (called VisualItems) are generated, which, in addition to the attributes of the source data, record visual properties such as location, colour, and size. Individual filters are provided in [Prefuse as Action modules, discussed later in this section.]

Filtering

Filtering is the process of mapping abstract data to a representation suitable for visualization. [...] Next, corresponding visual analogues (called VisualItems) are generated, which, in addition to the attributes of the source data, record visual properties such as location, color, and size. Individual filters are provided in prefuse as Action modules, discussed later in this section.

 Anmerkungen Starting with figure 6.2, which indeed is an exact copy (together with its caption) from Heer et al. (2005), after an "intermission" containing a paragraph which Nm seems to have authored himself (to paraphrase what could be found in the source), Nm continues his word-for-word "take-over" from Heer et al. (2005). Sichter (Graf Isolan), Bummelchen

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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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6.4 SUBGROUP DETECTION [FN 33]

A terrorist network can often be partitioned into cells (subgroups) consisting of individuals who closely interact with each other. Given a network, traditional data mining techniques such as cluster analysis may be employed to detect underlying groupings that are not otherwise apparent in the data. Hierarchical clustering methods have been proposed to partition a network into subgroups (Wasserman, S., and Faust, K., 1994). Cliques whose members are fully or almost fully connected can also be detected based on clustering results.

[FN 33] The most of the material presented in this Section is already published in (Memon N., Larsen Henrik Legind, 2006c)

[p. 104]

Subgroup detection

A criminal network can often be partitioned into subgroups consisting of individuals who closely interact with each other. Given a network, traditional data mining techniques such as cluster analysis may be employed to detect underlying groupings that are not otherwise apparent in

[p. 105]

the data. Hierarchical clustering methods have been proposed to partition a network into subgroups [EN 11]. Cliques whose members are fully or almost fully connected can also be detected based on clustering results.

[EN 11] Wasserman, S., and Faust, K. Social Network Analysis: Methods and Applications (Cambridge: Cambridge University Press, 1994).

 Anmerkungen Indeed: not only does this paragraph appear here in Nm's thesis but also in a number of Nm's papers, the earliest being a contribution to a conference in November 2005 and appearing here [2]. Thus the formulation by Xu and Chen (2005a) presented here still predates any analogous text by Nm by at least several months. Nm's only contribution has been to change the object of research from "criminals" to "terrorists". This furthermore begs the question if Nm's iMiner software prototype, which is at the heart of his thesis is far from Xu and Chen's CrimeNet explorer. Sichter (Graf Isolan), Bummelchen

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In this research and development study, we employed SNA techniques for terrorist intelligence analysis. The goal has been to provide law enforcement and intelligence agencies with third-generation network analysis techniques that not only produce graphical representations of terrorist networks but also provide structural analysis functionality to facilitate terrorist investigations. Several data mining projects in the COPLINK research have begun to employ these SNA techniques for criminal network analysis. The goal has been to provide law enforcement and intelligence agencies with third-generation network analysis techniques that not only produce graphical representations of criminal networks but also provide structural analysis functionality to facilitate crime investigations.
 Anmerkungen continues the take-over from Xu and Chen (2005a) (but from a different page than before) Sichter (Graf Isolan), Bummelchen

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[The Prefuse is] part of a larger move to systematize information visualization research and bring more interactivity into data analysis and exploration.

The Prefuse is open-source software. The toolkit, source code, and both interactive and video demonstrations are available at http://Prefuse.sourceforge.net.

prefuse is part of a larger move to systematize information visualization research and bring more interactivity into data analysis and exploration.

prefuse is open-source software. The toolkit, source code, and both interactive and video demonstrations are available at http://prefuse.sourceforge.net.

 Anmerkungen continued from previous page; this section concludes both the take-over and the original paper. Sichter (Graf Isolan), 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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ActionLists can be configured to run once, or to run periodically for a specified duration.

The execution of ActionLists is managed by a general activity scheduler, implemented using the approach of (Hudson, S. and J.T. Stasko, 1993). The scheduler accepts Activity objects (a superclass of ActionList), parameterized by start time, duration, and step rate, and runs them accordingly. The scheduler runs in a dedicated thread and oversees all active Prefuse visualizations, ensuring atomicity and helping avoid concurrency issues. A listener interface enables other objects to monitor activity progress, and pacing functions (Hudson, S. and J.T. Stasko, 1993) can be applied to parameterize animation rates (e.g., to provide slow-in slow-out animation).

6.3.6 Rendering and Display

Renderes draws VisualItems on the screen using the visual attributes of an item, for example, location, colour, to determine its actual on-screen appearance.

Renderers have a simple API consisting of three methods: one to draw an item, one to return a bounding box for an item, and one to indicate if a given point is contained within an item. Prefuse includes Renderers for drawing basic shapes, straight and curved edges, text, and images, including image loading, scaling, and caching support. Implementing user can also define their own renders by extending existing Renderers, or by implementing the Renderer interface to implement custom behaviour.

Mappings between items and appearances are managed by a RendererFactory: given a VisualItem, the RendererFactory returns an appropriate Renderer. This layer of indirection affords a high level of flexibility, allowing many simple Renderers to be written and then doled out as needed. It also allows visual appearances to be easily changed, either by issuing different Renderers in response [to data attributes, or by changing the RendererFactory for a given ItemRegistry.]

[p. 4]

ActionLists can be configured to run once, or to run periodically for a specified duration.

[p. 5]

The execution of ActionLists is managed by a general activity scheduler, implemented using the approach of [EN 24]. The scheduler accepts Activity objects (a superclass of ActionList), parameterized by start time, duration, and step rate, and runs them accordingly. The scheduler runs in a dedicated thread and oversees all active prefuse visualizations, ensuring atomicity and helping avoid concurrency issues. A listener interface enables other objects to monitor activity progress, and pacing functions [EN 24] can be applied to parameterize animation rates (e.g., to provide slow-in slow-out animation).

Rendering and Display

VisualItems are drawn to the screen by Renderers, components that use the visual attributes of an item (e.g., location, color) to determine its actual on-screen appearance. Renderers have a simple API consisting of three methods: one to draw an item, one to return a bounding box for an item, and one to indicate if a given point is contained within an item. prefuse includes Renderers for drawing basic shapes, straight and curved edges, text, and images (including image loading, scaling, and caching support). Custom rendering can be achieved by extending existing Renderers, or by implementing the Renderer interface.

Mappings between items and appearances are managed by a RendererFactory: given a VisualItem, the RendererFactory returns an appropriate Renderer. This layer of indirection affords a high level of flexibility, allowing many simple Renderers to be written and then doled out as needed. It also allows visual appearances to be easily changed, either by issuing different Renderers in response to data attributes, or by changing the RendererFactory for a given ItemRegistry.

[En 24] Hudson, S. and J.T. Stasko. Animation Support in a User Interface Toolkit: Flexible, Robust, and Reusable Abstractions. UIST’93. pp. 57-67, 1993.

 Anmerkungen continued from previous page; The whole page is a nearly left intact copy of material from Heer et al. (2005). The first sentence of 6.3.6 contains a typo, and that is in a part that was re-written. Sichter (Graf Isolan), WiseWoman

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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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[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

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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 [4], [5]. 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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To ensure performance, the ItemRegistry also recycles item instances when they are removed from the registry, avoiding object initialization costs that can cripple performance.

6.3.4 Actions

Actions are basic components of application design in Prefuse. Actions are composable processing modules that update the VisualItems in an ItemRegistry. Actions are the mechanism for selecting visualized data and setting visual properties, performing tasks such as filtering, layout, colour assignment, and interpolation. To facilitate extensibility, Actions follow a simple API: a single run method that takes an ItemRegistry and an optional fraction indicating animation progress as input. In addition, base classes for specific Action types such as filters and layout algorithms are provided. While Actions can perform arbitrary processing tasks, most fall into one of three types: filter, assignment, and animator actions. Filter actions performs tasks like filtering as described in previous subsections. Assignment actions are used to set attributes of VisualItems, for example: its position or colour. A variety of layout management techniques are also coded as actions in Prefuse. Animator actions interpolate visual attributes between starting and ending values to achieve animation, using the animation fraction provided by the Action interface. Prefuse includes animators for locations, colours, fonts, and sizes.

6.3.5 ActionLists and Activities

ActionLists are runnable routines which contain actions. A user may add as many Actions to ActionList as one wants. ActionList sequentially execute them as a sequential pipeline executing resources. These lists form processing pipelines that are invoked in response to user or system events. ActionLists are Actions themselves, allowing lists to be used as sub-routines of other lists [and recursion.]

To ensure performance, the ItemRegistry also recycles item instances when they are removed from the registry, avoiding object initialization costs that can cripple performance.

Actions

The basic components of application design in prefuse are Actions: composable processing modules that update the VisualItems in an ItemRegistry. Actions are the mechanism for selecting visualized data and setting visual properties, performing tasks such as filtering, layout, color assignment, and interpolation. To facilitate extensibility, Actions follow a simple API: a single run method that takes an ItemRegistry and an optional fraction indicating animation progress as input. In addition, base classes for specific Action types such as filters and layout algorithms are provided. While Actions can perform arbitrary processing tasks, most fall into one of three types: filter, assignment, and animator actions.

Filter actions perform the filtering process discussed earlier, controlling what entities and relations are represented by VisualItems in the ItemRegistry [...]

Assignment actions set visual attributes, such as location, color, font, and size, for VisualItems. prefuse includes extensible color, font, and size assignment functions and a host of layout techniques for positioning items.

Finally, animator actions interpolate visual attributes between starting and ending values to achieve animation, using the animation fraction provided by the Action interface. prefuse includes animators for locations, colors, fonts, and sizes.

ActionLists and Activities

To perform data processing, Actions are composed into runnable ActionLists that sequentially execute these Actions. These lists form processing pipelines that are invoked in response to user or system events. ActionLists are Actions themselves, allowing lists to be used as sub-routines of other lists.

 Anmerkungen Continuation from previous page. The second portion is marginally rewritten. Sichter (Graf Isolan), 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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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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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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* Simple interface between a binary relational storage structure and other modules of a database management system consists of three procedures:
• insert (triple); (ii) delete (partial specification of triple); (iii) retrieve partial specification of triple)
• Retrieval requests such as “list of all that terrorists met at Malaysia before 9/11” are met by issuing simple retrieval call (terrorist. met at Malaysia before 9/11. ?) which delivers only that data which has been requested.
Simple interface with other modules

Interface between a binary-relational storage structure and other modules of a database management system consists of three procedures: (i) insert (triple); (ii) delete (partial specification of triple); (iii) retrieve (partial specification of triple).

Retrieval requests such as 'list all employees of IBM' are met by issuing a simple retrieval call (IBM. employs. ?) which delivers only that data which has been requested

 Anmerkungen The source is named on the previous page. Nevertheless it is not to be expected that Nm, follows it word-for-word, given there are no quotation marks. Sichter (Graf Isolan), Hindemith

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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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[Individual filters are provided in] Prefuse as Action modules, discussed later in this section.

In the data state model of (Chi, E.H., 2000), filtering is made up of the Visualization Transformation: reducing abstract data to visualizable content. Filtering can also be understood as implementing a tiered version of the model-view-controller pattern (Krasner, G.E. and S.T. Pope, 1988). Abstract data provides a base model for any number of visualizations, while filtered data constitutes a visualization-specific model with its own set of view controllers. This enables multiple visualizations of a shared data set by using separate filters, and different views of a specific visualization by reusing the same filtered items, while isolating filtering logic away from the main application logic

6.3.3 Managing Visual Items: The ItemRegistry

Prefuse provides three types of VisualItem by default: NodeItems to visualize individual entities, EdgeItems to visualize relations between entities, and AggregateItems to visualize aggregated groups of entities. These items are arranged in a graph structure separate from the source data, maintaining a local version of the data topology and thereby enabling flexible representations of visualized content. If desired, additional VisualItem types can also be introduced.

VisualItems are recorded centrally in “ItemRegistry”. ItemRegisrty [sic!] data structure contains the overall state for a specific visualization. Filter Actions request visual analogues from the registry, which returns the VisualItems, creating them as needed. ItemRegistry can be viewed as a mapping between the abstract data and VisualItems. The ItemRegistry also contains a FocusManager.

[p. 3]

Individual filters are provided in prefuse as Action modules, discussed later in this section.

In the data state model of [EN 15], filtering constitutes the Visualization Transformation: reducing abstract data to visualizable content. Filtering can also be understood as implementing a tiered version of the model-view-controller pattern [EN 29]. Abstract data provides a base model for any number of visualizations, while filtered data constitutes a visualization-specific model with its own set of viewcontrollers. This enables multiple visualizations of a shared data set by using separate filters, and different views of a specific visualization by reusing the same filtered items.

[p. 4]

Managing Visual Items: The ItemRegistry

prefuse provides three types of VisualItem by default: NodeItems to visualize individual entities, EdgeItems to visualize relations between entities, and AggregateItems to visualize aggregated groups of entities. These items are arranged in a graph structure separate from the source data, maintaining a local version of the data topology and thereby enabling flexible representations of visualized content. If desired, additional VisualItem types can also be introduced.

VisualItems are created and stored in a centralized data structure called the ItemRegistry, which houses all the state for a specific visualization. Filter Actions request visual analogues from the registry, which returns the VisualItems, creating them as needed, and records the mapping between the abstract data and visualized content. The ItemRegistry also contains a FocusManager, [...]

---

[EN 15] Chi, E.H. A Taxonomy of Visualization Techniques Using the Data State Reference Model. InfoVis '00. pp. 69-75 2000.

[EN 29] Krasner, G.E. and S.T. Pope, A Description of the Model-View-Controller User Interface Paradigm in the Smalltalk-80 System. Journal of Object-Oriented Programming, 1988. 1(3): p. 26-49.

 Anmerkungen Continuation from previous page. A typo is to be found, where the text is not taken identically from the source. Interesting: a corresponding phrase in both texts (Visualization Transformation) is in italics. Sichter (Graf Isolan), 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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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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[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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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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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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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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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 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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7.4.2 Weaknesses of Open Source Knowledge bases

As discussed above, the original knowledge base has some important strength; this study also recognizes that it also has significant weaknesses that need to be understood when drawing conclusions from data. Two types of weaknesses are especially important. First, all major open source databases rely on data culled from news resources; they are likely biased toward the most newsworthy forms of terrorism (Fowler, W. W., 1981).

Although the original knowledge base under discussion, includes events that were prevented by authorities (for example the Bojinka terrorists plan), it is certain that some potential terrorist attacks never came to the attention of the media and are thus excluded. A related issue is that the original knowledge base includes incidents covered by media where criminals/ terrorists remain undisclosed. Without information concerning a criminal/ terrorist it may be difficult to accurately classify an incident as terrorism. Also, various media accounts of similar terrorist incidents may contain conflicting information. Without measures of reliability in news reporting, it is difficult for researchers to distinguish, which source supplies the most perfect account.

The second issue is that the original knowledge base lacks information on other important issues associated with each terrorism incident. Open source databases also lack information on the psychological characteristics, recruitment, and careers of members of terrorist movements. A lack of data on terrorist networks is mainly explained by their covert nature.

4.2 Weaknesses of Open Source Terrorism Databases

But while the PGIS data has some important strengths, we also recognize that it also has important weaknesses that need to be understood when drawing conclusions from the data. Three types of weaknesses are especially important. First, all the major open source terrorism databases (ITERATE, MIPT-RAND and PGIS) rely on data culled from news sources, thus they are likely biased toward the most newsworthy forms of terrorism [6]. Although the PGIS database includes events that were prevented by [page 411] authorities, it is certain that some potential terrorist attacks never came to the attention of the media and are thus excluded. A related issue is that the PGIS database includes incidents covered by the media where the perpetrator remains unidentified. Without information concerning the perpetrator it may be difficult to accurately classify the incident as terrorism, since the definition relies on the motive of the attacker. Finally, various media accounts of similar terrorist incidents may contain conflicting information. Without measures of reliability in news reporting, it is difficult for researchers to discern which source supplies the most accurate account.

The second issue is that the dataset lacks information on other important issues associated with each terrorism incident. Open source databases, including the one created by PGIS also lack information on the “psychological characteristics, recruitment, and careers of members of terrorist movements” [9:28]. [...] Of course, the lack of data on terrorist groups is mainly explained by their clandestine nature.

[6]. Fowler, W. W. (1981). [...]

 Anmerkungen The text has been copied with only slight adaptations, the source is not mentioned in the thesis anywhere Sichter (Hindemith), Bummelchen

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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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[The weakness of the] knowledge base includes potential media bias and misinformation, lack of information beyond incident specific details alone, and missing data which was not available in the media. We review some of these strength and weaknesses in the next section of this article. Weaknesses of the database include potential media bias and misinformation, lack of information beyond incident specific details alone, and missing data from a set of cards that were lost during an office move of PGIS. We review some of these strengths and weaknesses in the next section of this report.
 Anmerkungen The source is not mentioned anywhere in the thesis Sichter (Hindemith), 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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[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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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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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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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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The network structure may impact the ability of an organization to endure over the years and to complete attacks. It is important for intelligence agencies to understand how to fragment a network; they could potentially exploit the destabilizing techniques discussed in this research work including small world topology by eliminating weak ties in order to isolate the network and diminish its reach and power. The removal of individuals in key network regions may be even more important than attacking the traditional leaders of a group. The network structure may impact the ability of an organization to endure over the years and complete attacks. It is important for intelligence analysts to understand how to break up a network; they could potentially exploit the small world topology by eliminating weak ties in order to isolate the network and diminish its reach and power. The removal of individuals in key network locations may be even more important than attacking the traditional leaders of

a group.

 Anmerkungen continued from previous page Sichter (Graf Isolan), Hindemith

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Investigative data mining can also be used to understand the psychological effects of terrorism. One of the main effects of terrorism is fear, which is spread through network structures such as media, the Internet, and personal relationships. For example, the number of ties an individual has to victims of terrorism may impact the individual’s perception of the risk of terrorism.

It is believed to see further research on network structure evolution. It would be interesting to compare structures of multiple terrorist [networks to see how they evolve over time.]

[p. 7]

Network analysis can also be used to understand the psychological effect of terrorism. One of the main effects of terrorism is fear, which is spread through network

[p.8]

structures such as media, the Internet, and personal relationships. For example, the number of ties an individual has to victims of terrorism may impact the individual’s perception of the risk of terrorism. Finally, I would like to see further research on network structure evolution. It would be interesting to compare the structure of multiple terrorist networks to see how they evolve over time.

 Anmerkungen On the final stretch of Nm's thesis one can find a large portion of somebody else's final thoughts on the subject. Sichter (Graf Isolan), Hindemith

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[It is more helpful, however, to understand how a network recruits human bombs and] why people wish to join terrorist networks.

It is believed to see an expansion of the research areas in which investigative data mining is being used with regard to terrorism. Only a limited amount of work has been completed and there is much room for this technology to yield insights into terrorism.

It is more helpful, however, to understand how networks recruit participants and why people wish to join terrorist networks.

I would like to see an expansion of the research areas in which network analysis is being used with regard to terrorism. Only a limited amount of work has been completed, and there is much room for this tool to yield great insights into terrorism.

 Anmerkungen Even final words are not Nm's own. 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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[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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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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[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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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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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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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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[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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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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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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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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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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[... the following roles in terrorist networks as proposed by Williams (2001):]

1) Organizers are the core ensuring a network’s direction. They are the people who determine the scale and scope of activities, as well as the guidance and motivation necessary for performing those actions.

2) Insulators are individuals or groups charged with insulating a core member from dangers posed by infiltration and cooperation situations to which it is exposed. These actors communicate commands or direction from a core member to a foot-soldier. They also ensure that the flow of communication from a foot soldier in no way compromises a core member.

3) Communicators are individuals who guarantee that message flows effectively from one actor to another throughout the network. Unlike insulators, communicators must collect response regarding directives that they transmit to other actors in a network. Williams claims that there can be conflicts between those who act as insulators and those who act as communicators, or that the same individuals may assume both roles simultaneously to avoid these conflicts.

4) Guardians guarantee network security and take essential measures to diminish its vulnerability to infiltrations or external attack.

Williams (2001: 82-84) proposes the identification of a certain number of roles [...]

1) The organizers are the core ensuring the network’s direction. It is they who determine the scale and scope of activities, as well as the guidance and impetus necessary for performing those activities.

2) Insulators are the individuals or groups charged with insulating the core from the danger posed by the infiltration and compromise to which it is exposed. These actors transmit directives or guidance from the core to the periphery. They also ensure that the flow of communication from the periphery in no way compromises the core.

3) Communicators are individuals who ensure that communication flows effectively

[Page 13]

from one actor to another throughout the network. Unlike the insulators, they must gather feedback regarding directives that they transmit to other actors in the network. Williams claims that there can be conflicts between those who act as insulators and those who act as communicators, or that the same individuals may assume both roles simultaneously to avoid these conflicts.

4) Guardians ensure network security and take necessary measures to minimize its vulnerability to infiltrations or external attack.

 Anmerkungen Williams (2001) is given as source, but this reference as well as many formulations are taken from Lemieux (2003) without reference. In particular the sentence starting "Williams claims ..." is word by word taken from the source, which demonstrates that the text has been taken from Lemieux (2003) and not Williams (2001). Sichter (Hindemith), Bummelchen

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[Their role also consists in watching over recruitment to a] network and ensuring the loyalty of recruits through a variety of procedural promises and hidden pressure directed against new members and their families. Guardians pursue to avoid defections from the network actors and to reduce losses when defections occur.

5) Extenders extend the network by recruiting new members and also by negotiating association with other networks and encouraging association with the business sector, government and justice. Various strategies are used to this end. They range from unpaid recruitment through corruption and bribery to involuntary recruitment through pressure, infrequently supported by encouragements and prizes.

6) Monitors are devoted to the network’s usefulness their tasks consist in providing information to organizers regarding flaws and hitches within the network so that the organizers can decide them. Monitors guarantee that the network is able to correct to new situations and keep the high degree of elasticity that is essential to avoid law enforcement.

7) Crossovers are part of a terrorist network, but continue to work in legal institutions, whether governmental, financial or commercial. As such, these individuals provide vital evidence and contribute to the guard of a network.

Their role also consists in watching over recruitment to the network and ensuring the loyalty of recruits through a variety of ritual oaths and latent coercion directed against new members and their families. Guardians seek to prevent defections from the network actors and to minimize damages when defections occur.

5) Extenders extend the network by recruiting new members and also by negotiating collaboration with other networks and encouraging collaboration with the business sector, government and justice. Various tactics are used to this end. They range from voluntary recruitment through bribery and corruption to involuntary recruitment through coercion, occasionally supported by incentives and rewards.

6) Monitors are dedicated to the network’s effectiveness their responsibilities consist in providing information to organizers regarding weaknesses and problems within the network so that the organizers can resolve them. Monitors ensure that the network is able to adjust to new circumstances and maintain the high degree of flexibility that is necessary to circumvent law enforcement.

7) Crossovers are part of a criminal network, but continue to work in legal institutions, whether governmental, financial or commercial. As such, these individuals provide invaluable information and contribute to the protection of the network.

 Anmerkungen Refer also to the previous page: Nm/Fragment_246_08. No source is given Sichter (Hindemith), Bummelchen

 Bearbeitet: 21. April 2012, 22:39 HindemithErstellt: 12. April 2012, 20:09 (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,