12 gesichtete, geschützte Fragmente: Plagiat
| [1.] Nm3/Fragment 1037 35 - Diskussion Bearbeitet: 29. January 2014, 17:32 Graf Isolan Erstellt: 29. January 2014, 13:36 (Hindemith) | Balasundaram et al 2006, Fragment, Gesichtet, Nm3, SMWFragment, Schutzlevel sysop, Verschleierung |
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| Cohesion analysis (also called structural 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 (Wasserman, S., Faust, K, 1994). Cohesion analysis is also believed to influence emergence of consensus among group members.
25. Wasserman, S., Faust, K.: Social Network Analysis. Cambridge University Press.1994. |
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.
52. Wasserman, S., Faust, K.: Social Network Analysis. Cambridge University Press (1994) |
The source is not mentioned. To be continued on the next page. |
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| [2.] Nm3/Fragment 1038 01 - Diskussion Bearbeitet: 29. January 2014, 20:51 WiseWoman Erstellt: 29. January 2014, 13:42 (Hindemith) | Balasundaram et al 2006, Fragment, Gesichtet, Nm3, SMWFragment, Schutzlevel sysop, Verschleierung |
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| [Examples of cohesive sub-] groups include religious cults, terrorist cells, criminal gangs, military platoons, tribal groups and work groups etc.
[...] Some direct application areas of social networks include studying terrorist networks (Sageman, M., 2004, Berry, N. et al., 2004), which is essentially an [sic] special application of criminal network analysis that is intended to study organized crimes such as terrorism, drug trafficking and money laundering (McAndrew, D., 1999, Davis, R. H., 1981). Concepts of social network analysis provide suitable data mining tools for this purpose (Chen, H., et al., 2004). 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 by Valdis Krebs (Krebs, V., 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. |
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]. 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. 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. 9. Berry, N., Ko, T., Moy, T., Smrcka, J., Turnley, J., Wu, B.: Emergent clique formation in terrorist recruitment. The AAAI-04 Workshop on Agent Organizations: Theory and Practice, July 25, 2004, San Jose, California (2004). Http://www.cs.uu.nl/ virginia/aotp/papers.htm [sic] 17. Chen, H., Chung, W., Xu, J.J., Wang, G., Qin, Y., Chau, M.: Crime data mining: A general framework and some examples. Computer 37(4), 50–56 (2004) 21. Davis, R.H.: Social network analysis: An aid in conspiracy investigations. FBI Law Enforcement Bulletin pp. 11–19 (1981) 32. Krebs, V.: Mapping networks of terrorist cells. Connections 24, 45–52 (2002) 36. McAndrew, D.: The structural analysis of criminal networks. In: D. Canter, L. Alison (eds.) The Social Psychology of Crime: Groups, Teams, and Networks, Offender Profiling Series, III. Aldershot, Dartmouth (1999) 43. Sageman, M.: Understanding Terrorist Networks. University of Pennsylvania Press (2004) |
The source is not given. The copied text starts on the previous page: Nm3/Fragment_1037_35. Figure 1 is different in each text. |
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| [3.] Nm3/Fragment 1039 02 - Diskussion Bearbeitet: 1. February 2014, 11:06 Graf Isolan Erstellt: 29. January 2014, 14:27 (Hindemith) | Fragment, Gesichtet, Nm3, Penzar Srbljinovic 2005, SMWFragment, Schutzlevel sysop, Verschleierung |
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| After tragic terrorist attacks by kidnapped 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” (Memon N., H. L. Larsen, 2006), extremely hard to destroy, especially by traditional anti-terrorist / counterterrorist methods. 19. Memon, N. Henrik Legind Larsen [sic], Practical Algorithms for Destabilizing Terrorist Networks, Lecture Notes in Computer Science (LNCS) 3975, ISI 2006, Eds. S. Mehrotra et al. pp. 389-400, 2006. |
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. |
The source is not given anywhere in the paper. The authors refer here to themselves, although this text cannot be found in the given source, not even the term "atypical organization". |
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| [4.] Nm3/Fragment 1039 17 - Diskussion Bearbeitet: 1. February 2014, 20:29 Hindemith Erstellt: 1. February 2014, 13:32 (Graf Isolan) | BauernOpfer, Combating Terrorism Center 2006, Fragment, Gesichtet, Nm3, SMWFragment, Schutzlevel sysop |
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| According to many counterterrorism analysts today, 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).
Al Qaeda did not decide to decentralize until 2002, following the ouster 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). 9. Grier, P. “The New Al Qa’ida: Local Franchiser,” Christian Science Monitor (11 July 2005). Online at: http://www.csmonitor.com/2005/ 0711/p01s01-woeu.html (Accessed on May 26, 2006). 11. Joseph Felter et. al., Harmony and Disharmony: Exploiting al-Qa’ida’s Organizational Vulnerabilities (West Point, N.Y.: United States Military Academy, 2006), p. 7-9. 26. Wiktorowicz, Q. "The New Global Threat: Transnational Salafis and Jihad," Middle East Policy 8, no. 4 (2001: 18-38) |
[page 8]
According to most counterterrorism analysts today, al-Qa’ida has evolved from a centrally directed organization into a worldwide franchiser of terrorist attacks.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. [page 9] 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,” who directed training and placement for the group, was captured in Faisalabad, Pakistan, in February 2002. Ramzi Bin al Shibh, the organizer of the Hamburg, Germany cell that formed the core of the 9/11 hijackers, was captured in Karachi, Pakistan, on the first anniversary of the attacks. These and other counterterrorism successes ultimately led to the capture of Khalid Sheik Mohammed, the mastermind of 9/11 and the financier of the first World Trade Center attack, in Rawalpindi, Pakistan, in March 2003. And a month later, Tawfiq Attash Kallad, the mastermind of the USS Cole attack, was apprehended in Karachi. 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 and move its operations out over the entire world.10
7 Peter Grier, “The New Al Qa’ida: Local Franchiser,” Christian Science Monitor (11 July 2005). Online at: http://www.csmonitor.com/2 005/0711/p01s01-woeu.html. 10 Robert Windrem, 2005. |
Even though the source is given, citations are not marked as such and the extent of the usage of the source is not clear to the reader. |
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| [5.] Nm3/Fragment 1039 31 - Diskussion Bearbeitet: 30. January 2014, 07:16 Klgn Erstellt: 29. January 2014, 14:46 (Hindemith) | Fragment, Gesichtet, Koschade 2005, Nm3, SMWFragment, Schutzlevel sysop, Verschleierung |
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| Investigative Data Mining (IDM) offers the ability to firstly map a covert cell, and to secondly measure the specific structural and interactional criteria of such a cell. This framework aims to connect the dots between individuals and “map and measure [complex, covert, human groups and organisations”.] | 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”.8 8 Krebs, V. (2002) “Mapping Networks of Terrorist Cells”, Connections, Vol. 24, 3, pp. 43-52. |
To be continued on the next page: Nm3/Fragment_1040_01 The reference to Krebs (2002) has been removed. |
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| [6.] Nm3/Fragment 1040 01 - Diskussion Bearbeitet: 29. January 2014, 18:51 Graf Isolan Erstellt: 29. January 2014, 14:54 (Hindemith) | Fragment, Gesichtet, KomplettPlagiat, Koschade 2005, Nm3, SMWFragment, Schutzlevel sysop |
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| 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”.
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. In short IDM “provides a useful way of structuring knowledge and framing further research. Ideally it can also enhance an analyst’s predictive capability”. |
[page 2]
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] The method focuses on uncovering the patterning of people’s interaction,9 and correctly interpreting these networks assists “in predicting behaviour and decision-making within the network”.10 [...] 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”.12 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. 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. 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. |
The source is not given. Several references have been removed, although the quotation marks indicate that two passages come from other (unnamed) authors. |
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| [7.] Nm3/Fragment 1040 13 - Diskussion Bearbeitet: 29. January 2014, 23:36 Hindemith Erstellt: 29. January 2014, 15:18 (Hindemith) | Fragment, Gesichtet, Mukherjee Holder 2004, Nm3, SMWFragment, Schutzlevel sysop, Verschleierung |
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| Covert networks like terrorist networks remain mingled with socially oriented networks (like families, organizations etc.) in the real world. The buzz word for covert networks is “secrecy” and hence to discover such networks (technically, to discern distinctive patterns in the activities and communications of such dark networks) can be very tricky and often misleading due to unavailability of authentic data or in some cases availability of “doctored” data. This issue has especially blown up in the recent past and after the September 11, 2001 tragedy, it has been in the limelight so much so that it is worthwhile to take a close look at the distinguishing properties of such networks. For Example:
(1) In bright networks, actors who are highly central are typically the most important ones. On the contrary, peripheral players (or “boundary spanners” as they are typically called) may be huge resources to a terrorist group although they receive very low network centrality scores. This is because they are well positioned to be innovators, since they have access to ideas and information flowing in other clusters. Similarly, in an organization, these peripheral employees are in a position to combine different ideas and knowledge into new products and services. They may be contractors or vendors who have their own network outside of the company, making them very important resources for fresh information not available inside the company (Krebs V., 2002, Hanneman, R., 2000). (2) The role of a “broker” (Krebs V., 2002) is a very powerful role in a social network as it ties two hitherto unconnected constituencies / groups together but of course, it is a single-point of failure. These broker type roles are often seen in terrorist networks. Such nodes are also referred to as “cutpoints” (Hanneman, R., 2000). 10. Hanneman, R. E., Introduction to Social Network Methods. Online Textbook Supporting Sociology 175. Riverside, CA: University of California, 2000. 13. Krebs, V.: Mapping networks of terrorist cells. Connections 24, 45–52, 2002. |
Covert networks remain mingled with socially-oriented networks (like families, organizations etc.) in the real world. The buzz word for covert networks is “secrecy” and hence to discover such networks (technically, to discern distinctive patterns in the activities and communications of such illegitimate groups) can be very tricky and often misleading due to unavailability of authentic data or in some cases availability of “doctored” data. This issue has especially blown up in the recent past and after the September 11, 2001 tragedy, it has been in the limelight so much so that it is worthwhile to take a close look at the distinguishing properties of such networks.
[...] (3) In legitimate networks, actors who are highly central are typically the most important ones. On the contrary, peripheral players (or “boundary spanners” as they are typically called) may be huge resources to a terrorist group although they receive very low network centrality scores. This is because they are well-positioned to be innovators, since they have access to ideas and information flowing in other clusters. Similarly, in an organization, these peripheral employees are in a position to combine different ideas and knowledge into new products and services. They may be contractors or vendors who have their own network outside of the company, making them very important resources for fresh information not available inside the company [5, 8]. [page 49] (9) The role of a “broker” [8] is a very powerful role in a social network as it ties two hitherto unconnected constituencies/groups together but of course, it is a single-point of failure. These broker-type roles are often seen in terrorist networks. Such nodes are also referred to as “cutpoints” [5]. [5] Robert Hanneman: Introduction to Social Network Methods, Department of Sociology, University of California, Riverside. (URL: http://faculty.ucr.edu/~hanneman /SOC157/Software/NETTEXT.PDF). [8] Valdis E. Krebs: Uncloaking Terrorist Networks (URL: http://www.firstmonday.dk/ issues/issue7_4/krebs). First Monday, volume 7, number 4, April 2002. |
The source is not given. Indeed, the number of Hanneman's undergraduate sociology course was 157 and not 175, as Nm3 claims. |
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| [8.] Nm3/Fragment 1041 16 - Diskussion Bearbeitet: 29. January 2014, 22:12 WiseWoman Erstellt: 29. January 2014, 13:51 (Hindemith) | Balasundaram et al 2006, Fragment, Gesichtet, KomplettPlagiat, Nm3, SMWFragment, Schutzlevel sysop |
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| 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 (Luce, R., Perry A., 1949). 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 (Scott, J, 2000), Wasserman, S., Faust, K., 1994) and modeling disadvantages (Siedman [sic], S. B., Freeman, L. C., 1992).
Alternative approaches were suggested that essentially relaxed the definition of cliques. 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. Luce R. introduced a distance based model called n-clique (Luce, R., 1950). This model was also studied along with a variant called n-clan by Mokken (Mokken, R., 1979). However, their originally proposed definitions required some modifications to be more meaningful mathematically. |
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 [2,52] and modeling disadvantages [47,25].
Alternative approaches were suggested that essentially relaxed the definition of cliques. 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 and [2] introduced a diameter based model called k-club. These models were also studied along with a variant called k-clan by Mokken [38]. However, their originally proposed definitions required some modifications to be more meaningful mathematically. 2. Alba, R.: A graph-theoretic definition of a sociometric clique. Journal of Mathematical Sociology 3, 113–126 (1973) 25. Freeman, L.C.: The sociological concept of “group”: An empirical test of two models. American Journal of Sociology 98, 152–166 (1992) 34. Luce, R.: Connectivity and generalized cliques in sociometric group structure. Psychometrika 15, 169–190 (1950) 35. Luce, R., Perry, A.: A method of matrix analysis of group structure. Psychometrika 14, 95–116 (1949) 38. Mokken, R.: Cliques, clubs and clans. Quality and Quantity 13, 161–173 (1979) 47. Seidman, S.B., Foster, B.L.: A graph theoretic generalization of the clique concept. Journal of Mathematical Sociology 6, 139–154 (1978) 52. Wasserman, S., Faust, K.: Social Network Analysis. Cambridge University Press (1994) |
The source is not given. The text has been copied verbatim and also all references to the literature are identical with the source. |
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| [9.] Nm3/Fragment 1042 01 - Diskussion Bearbeitet: 1. February 2014, 11:08 Graf Isolan Erstellt: 29. January 2014, 13:59 (Hindemith) | Balasundaram et al 2006, Fragment, Gesichtet, Nm3, SMWFragment, Schutzlevel sysop, Verschleierung |
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| These drawbacks are pointed out and the models are appropriately redefined in (Balasundaram, B. et al, 2005). All these models emphasize the need for high reachability inside a cohesive subgroup and have their own merits and demerits as models of cohesiveness. In this paper we also discuss on a degree based model and called k-plex (Wasserman, S. et al, 2004). This model relaxes familiarity within a cohesive subgroup and implicitly provides reachability and robustness. | 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.
7. Balasundaram, B., Butenko, S., Trukhanov, S.: Novel approaches for analyzing biological networks. Journal of Combinatorial Optimization 10, 23–39 (2005) 47. Seidman, S.B., Foster, B.L.: A graph theoretic generalization of the clique concept. Journal of Mathematical Sociology 6, 139–154 (1978) |
The source is not given. Note the grammatical mistake "we also discuss on a ...", which has its roots in the formulation of the source. |
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| [10.] Nm3/Fragment 1045 01 - Diskussion Bearbeitet: 1. February 2014, 11:07 Graf Isolan Erstellt: 29. January 2014, 15:48 (Hindemith) | BauernOpfer, Fragment, Gesichtet, Latora and Marchiori 2004, Nm3, SMWFragment, Schutzlevel sysop |
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| The main idea is to use as a measure of the centrality of a node i the drop in the network efficiency caused by deactivation of the node. The importance I (nodei) of the ith [sic] node of the graph G is therefore:
I (nodei) ≡ Δ E = E (G) − E (G − nodei) , i = 1,...,N, (2) Where G − nodei indicates the network obtained by deactivating nodei in the graph G. The most important nodes, i.e. the critical nodes are the ones causing the highest ΔE. |
The main idea is to use as a measure of the centrality of a node i the drop in the network efficiency caused by the deactivation of the node. The importance I(nodei) of the ith node of the graph G is therefore:
I(nodei) ≡ ΔE = E(G) − E(G − nodei) , i = 1,...,N (2) whereby G - nodei we indicate the network obtained by deactivating nodei in the graph G. The most important nodes, i.e. the critical nodes are the ones causing the highest ΔE. |
The source is given in the previous paragraph in a way that suggests that the here documented paragraph summarizes content from the source. That it is taken verbatim from the source is not made clear, however. |
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| [11.] Nm3/Fragment 1045 33 - Diskussion Bearbeitet: 1. February 2014, 20:17 Hindemith Erstellt: 1. February 2014, 14:42 (Graf Isolan) | Fragment, Gesichtet, KomplettPlagiat, Newman 2006, Nm3, SMWFragment, Schutzlevel sysop |
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| The centrality measures address the question, “Who is the most important or central person in the network?” There are many answers to this question, depending on what we mean by important. Perhaps the simplest of centrality measures is degree centrality, also called simply degree.
Though simple, degree is often a highly effective measure of the influence or importance of a node: in many social settings people with more connections tend to have more power. A more sophisticated version of the same idea is the so-called eigenvector centrality (which is also known as centrality of a centrality). Where degree centrality gives a [simple count of the number of connections a vertex has, eigenvector centrality acknowledges that not all connections are equal.] |
[page 4]
Centrality measures address the question, “Who is the most important or central person in this network?” There are many answers to this question, depending on what we mean by important. Perhaps the simplest of centrality measures is degree centrality, also called simply degree. [...] Though simple, degree is often a highly effective measure of the influence or importance of a node: in many social settings people with more connections tend to have more power. A more sophisticated version of the same idea is the so-called eigenvector centrality. Where degree centrality gives a simple count of the number of connections a vertex has, eigenvector centrality acknowledges that not all connections are equal. |
Not a hint of the original source is given. |
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| [12.] Nm3/Fragment 1046 01 - Diskussion Bearbeitet: 1. February 2014, 20:19 Hindemith Erstellt: 1. February 2014, 19:34 (Graf Isolan) | Fragment, Gesichtet, KomplettPlagiat, Newman 2006, Nm3, SMWFragment, Schutzlevel sysop |
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| [Where degree centrality gives a] simple count of the number of connections a vertex has, eigenvector centrality acknowledges that not all connections are equal. | Where degree centrality gives a simple count of the number of connections a vertex has, eigenvector centrality acknowledges that not all connections are equal. |
Source is not given. Fragment presents the final sentence of the takeover begun in Nm3/Fragment_1045_33. |
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