Personalised PageRank as a Method of Exploiting Heterogeneous Network for Counter Terrorism and Homeland Security

Abstract

Majority of the social network analysis studies for counter-terrorism and homeland security consider homogeneous network. However, a terrorist activity (attack) is often defined by several attributes such as terrorist organisation, time, place, attack type etc. To capture inherent dependency between the attributes, we need to adopt a network which is capable of capturing the dependency between the attributes. In this paper, we define a heterogeneous network to represent a collection of terrorist activities. Further, we propose personalised PageRank (PPR) as a method capable of performing various analytical operations over heterogeneous network just by changing model parameters without changing the underlying model. Using global terrorist data (GTD), behavioural network, and news discussion network, we show various applications of PPR for counter-terrorism over heterogeneous network just by changing the model parameter. In addition we propose heterogeneous version of four local proximity based link prediction methods, namely, Common Neighbour, Adamic-Adar, Jaccard Coefficient, and Resource Allocation.