Terrorist Networks Analysis through Argument Driven Hypotheses Model

Abstract

Social network analysis has been used for quite some time to analyze and understand the behavior of nodes in the network. Theses nodes could be individuals or group of persons, events or organizations, etc. In fact these nodes could be any thing, importantly, these nodes propagate or do some thing and obviously these nodes have attributes. Social network analysis (SNA) is a multi-model multi-link problem and one can imagine the challenge posed by such multidimensional task. Typically, models represent various processes and their organization including the interaction between processes. Such types of models are intellective simulation models, explaining one particular aspect of the model abstracting other factors present in the model. The standard or normal representation of a typical social network model is through a graph data structure. The dynamics of larger social networks is so complex some time it becomes difficult to understand the various levels of interactions and dependencies just by mere representation through a graph. However, to overcome this limitation many analytical methods provide relationship dependencies, role of different nodes and their importance in the social networks. Since the start of the new century many terrorism events have occurred around the globe. These events have provided a new impetus for the analysis, investigation, studying the behavior and tracking terrorist networks (individuals). In this paper we are presenting a very novel and absolutely new approach to SNA for locating important key players in the network. The system also predicts a path through these nodes which shows the vulnerability of the network and if the path along with these nodes is removed it can reduce/destabilize or even destroy the structure of the network