This 2013 paper focuses on the modelling of terrorist networks online and addresses changes in technology and the associated creation of new security dynamics and threats.
This 2013 paper introduces a methodology that incorporates information available on terrorist networks, into the analysis of social networks underlying terrorist groups. The authors demonstrate that quantitative modeling by means of cooperative game theoretic centrality measures, enables the incorporation of this additional information to common structure of networks found in social network analysis. In view of identifying key players, cooperative game theory is utilized to develop rankings of individuals in terror networks based on both the structure of the terrorist network and additional information on individual terrorists and their relationships. The authors’ framework is applied to the Jemaah Islamiyah bombing in Bali and the 9/11 attacks in New York and Washington. The analyses of these two cases with degree centrality, betweeness centrality, and closeness centrality, concurrently with game theoretic connectivity centrality, shows that the latter provides a valuable contribution to the identification of key players in terrorist networks and is therefore useful in combating terrorist groups.
This article will be of use to practitioners and researchers looking at terrorist networks online and interested in methods for resource allocation to destabilize networks. It will be useful for those interested in methods of centrality analysis and social network analysis more generally. The authors introduce a weighted connectivity game that is able to take both the structure of the terrorist network as well as information about individual terrorists into account. By applying a game theoretic centrality measure to the weighted connectivity game, a ranking is produced of the players in the terrorist network. This allows for the optimal allocation of observation resources that the authors argue can be utilization to help destabilize a terrorist network, by identifying the highest-ranking members and potential removal of its highest-ranking members.