Practical Algorithms for Destabilizing Terrorist Networks

  • Nasrullah Memon
  • Henrik Legind Larsen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3975)


This paper uses centrality measures from complex networks to discuss how to destabilize terrorist networks. We propose newly introduced algorithms for constructing hierarchy of covert networks, so that investigators can view the structure of terrorist networks / non-hierarchical organizations, in order to destabilize the adversaries. Based upon the degree centrality, eigenvector centrality, and dependence centrality measures, a method is proposed to construct the hierarchical structure of complex networks. It is tested on the September 11, 2001 terrorist network constructed by Valdis Krebs. In addition we also propose two new centrality measures i.e., position role index (which discovers various positions in the network, for example, leaders / gatekeepers and followers) and dependence centrality (which determines who is depending on whom in a network). The dependence centrality has a number of advantages including that this measure can assist law enforcement agencies in capturing / eradicating of node (terrorist) which may disrupt the maximum of the network.


Complex Network Degree Centrality Social Network Analysis Centrality Measure Real World Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nasrullah Memon
    • 1
  • Henrik Legind Larsen
    • 1
  1. 1.Software Intelligence Security Research Center, Department of Software and Media TechnologyAalborg UniversityEsbjergDenmark

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