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Angaben zur Quelle [Bearbeiten]

Autor     Jennifer Xu, Byron Marshall, Siddharth Kaza, Hsinchun Chen
Titel    Analyzing and Visualizing Criminal Network Dynamics: A Case Study
Jahr    2004
Anmerkung    The article has been published also in: Intelligence and Security Informatics: Second Symposium on Intelligence and Security Informatics, ISI 2004, Tucson, AZ, USA, June 10-11, 2004 ; Proceedings, Band 2, Editor: Hsinchun Chen, ISBN 3-540-22125-5, Springer-Verlag Berlin, Pages 359-377 Google Books, the page numbers given here refer to the version that is available online.
URL    http://ai.arizona.edu/intranet/papers/isi2004networkdynamics.pdf

Literaturverz.   

no
Fußnoten    no
Fragmente    10


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

---

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

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

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

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

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

Anmerkungen

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

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

2.13 ANALYZING SOCIAL NETWORK DYNAMICS

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

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

[P. 4]

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

[...]

3. Literature Review

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

3.1 Analyzing social network dynamics

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


[Pp. 27-29]

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

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

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

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

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

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

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

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

Anmerkungen

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2.13.1 Descriptive methods

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

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

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

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

3.1.1 Descriptive methods

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

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


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

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

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

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

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

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

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

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

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

[Page 6]

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

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


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

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

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

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

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

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

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

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

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

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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). [...]

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2.13.2 Statistical Methods

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

3.1.2 Statistical Methods

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


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

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

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

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

[P. 7]

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

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


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

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

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

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

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2.13.3 Simulation Methods

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

3.1.3 Simulation Methods

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

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

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

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

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

[P. 8]

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


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

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

2.14 SUMMARY

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

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

[Page 10]

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

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