Abstract
Al Qaeda’s network structure before the tragic events of 9/11/2001 is studied using a method of social network analysis. The method is based on a modeling framework to assess the influence of a node in a complex network with respect to spreading information via different paths between source and target nodes. The same framework is used consistently to compute closeness and betweenness centrality measures as well as to detect subcommunities. Centrality measures taking into account all possible paths between source and target nodes, not just the shortest paths, are useful in modeling resilience of covert networks. Along these lines, new versions of node and link betweenness centrality measures are proposed.
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References
Agneessens, F., Borgatti, S.P., Everett, M.G.: Geodesic based centrality: unifying the local and the global. Soc. Netw. 49, 12–26 (2017)
Baker, W.E., Faulkner, R.: The social organization of conspiracy: illegal networks in the heavy electrical equipment industry. Am. Sociol. Rev. 58(6), 837–860 (1993)
Borgatti, S.P.: Identifying sets of key players in a social network. Comput. Math. Organ. Theor. 12, 21–34 (2006)
Borgatti, S.P., Everett, M.: A graph-theoretic perspective on centrality. Soc. Netw. 28, 466–484 (2006)
Coscia, M., Giannotti, F., Pedreschi, D.: A classification for community discovery methods in complex networks. Stat. Anal. Data Min. 4(5), 512–546 (2011)
Freeman, L.C.: Centrality in social networks: conceptual clarification. Soc. Netw. 1, 215–239 (1979)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. U S A 99(12), 7821–7826 (2002)
Karrer, B., Newman, M.E.J.: Stochastic blockmodels and community structure in networks. Phys. Rev. E 83(1), 016107 (2011)
Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell. Syst. Tech. J. 49, 291–307 (1970)
Krebs, V.E.: Mapping networks of terrorist cells. Connections 24(3), 43–52 (2001)
Krebs, V.E.: Uncloaking terrorist networks. First Monday 7(4) (2002)
Lancichinetti, A., Fortunato, S.: Community detection algorithms: a comparative analysis. Phys. Rev. E 80, 056117 (2009)
Newman, M.E.J.: A measure of betweenness centrality based on random walks. Soc. Netw. 27(1), 39–54 (2003)
Newman, M.E.J.: The structure and function of complex networks. SIAM Rev. 45, 167–256 (2003)
Newman, M.E.J.: Detecting community structure in networks. Eur. Phys. J B38(2), 321 (2004)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)
Newman, M.E.J.: Networks. An Introduction. Oxford University Press, Oxford (2010)
Ouellet, M., Bouchard, M., Hart, M.: Criminal collaboration and risk: the drivers of Al Qaeda’s network structure before and after 9/11. Soc. Netw. 51, 171 (2016)
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Kuikka, V. (2018). Terrorist Network Analyzed with an Influence Spreading Model. In: Cornelius, S., Coronges, K., Gonçalves, B., Sinatra, R., Vespignani, A. (eds) Complex Networks IX. CompleNet 2018. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-73198-8_16
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DOI: https://doi.org/10.1007/978-3-319-73198-8_16
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