Abstract: Majority of the studies on counter-terrorism using social network analysis consider homogeneous networks built over either terrorists or terrorist organizations. However, terrorist attacks are often defined using heterogeneous attributes such as location, time, target type, organization, etc. This paper constructs a heterogeneous terrorist attack network considering heterogeneous attributes and captures heterogeneous influences propagated from different attributes using Personalized PageRank. Personalized PageRank is a flexible model capable of propagating supervised information while traversing over a network using different personalized parameters. This study investigates effects of various parametric setups to study influence of various factors such as news media discussions, historical activities, temporal behavior etc., on one of the important counter-terrorism problems; prediction of future attacks of a terrorist organization. From various experimental observations, it is evident that news media discussion and network’s temporal behavior have a positive influence on future activities of a terrorist organization. Further, this paper investigates responses of various node proximity based link prediction methods on predicting future relationships between a terrorist organization with other attributes (such as country, city and target types). Majority of the studies on link prediction using node proximity ignore node importance. However, in a heterogeneous environment, nodes from different classes may have different importance. This paper proposes new variants of four proximity based link prediction methods, namely, Adamic Adar, Jaccard Coefficient, Resource Allocation, and Common Neighbor, which have the capability to incorporate node importance. With suitable experiments, we show that the proposed variants of link predictors are more accurate at predicting relations than their state of art counterparts.
Keywords: Heterogeneous network, social network analysis, counter-terrorism, personalized PageRank, external information
