Link Prediction Using Social Network Analysis over Heterogeneous Terrorist Network

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.