A Model for Predicting Terrorist Network Lethality and Cohesiveness

The recurrent nature of terrorist attacks in recent times has ushered in a new wave of study in the field of terrorism known as social network analysis (SNA). Terrorist groups operate as a social network in a stealthy manner to enhance their survival thereby making sure their activities are unperturbed. Graphical modeling of the terrorist network in order to uncloak their target is an NP-complete problem. In this study, we assume that the terrorist group usually confine themselves in a neighborhood that is conducive or regarded as a safe haven. The conducive nature of the environment ramified the terrorist action process from areas of low action (soft target) to areas of high action (maximally lethal target). It is the desired of every terrorist to be maximally lethal, therefore curbing terrorist activities becomes a crucial issue. This study explores the 9/11 event and examines the M-19 network that carried out the attack. We use a combination of methods to rank vertices in the network to show their role. Using the 4-centrality measures, we rank the vertices to get the most connected nodes in the network. The betweenness centrality and traversing graph shortest path are calculated as well. We opine that in curbing terrorist network activities, the removal of a vertex with high centrality measures is a condition sine qua non. We further examine the lethality of the network as well as network bonding to get the degree of cohesiveness which is a crucial determinant for network survival. The experimental results show that M-01 is the most important vertex in the network and that timing is a salient feature in unveiling network lethality. We posit that more study is required to include the global network that was involved in the 9/11 event.