Recognizing information spreaders in terrorist networks: 26/11 attack case study

Abstract

Terrorism is a man-made hazard characterized by its uncontrollability and unpredictability. In fact, terrorist cells are covert networks where secrecy is the prime concern during the operation. To disrupt these inhuman operations, it is crucial to reveal this secrecy and identify the responsible key actors. Therefore, a new research area emerges. Investigative Data Mining (IDM) is the study of terrorist networks using Social Network Analysis (SNA). It involves graph theory to analyze networks. Among analysis techniques, network metrics defined as centrality measures have been successfully involved in terrorist networks destabilization methods. In this paper, we propose another disruption strategy of terrorist network using the percolation centrality metric. This measure allows to conduct a dynamic analysis of terrorist network on one hand. On the other hand, it identifies information spreaders in the network. We experiment on the Mumbai 26/11 attack data set, the proposed approach recognizes the information spreaders involved in this incident.