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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.

Social network analysis (SNA) has been effectively used in counter-terrorism analysis by generating homogeneous network. In this paper, we consider a large dataset reporting various terrorist attacks over the globe and represent the dataset as a heterogeneous network. The objective of this paper is to the explore the effect of various link prediction frameworks such as topic modeling, network topology and graph kernels. We propose bipartite based link prediction over topic feature relationship, heterogeneous version of node proximity based link prediction and graph kernel methods. From various experimental observation, it is evident that bipartite method based on topic modeling also return comparable results (sometimes better) as that of node proximity and graph kernel.