Network Structure Mining: Locating and isolating core members in covert terrorist networks

Knowing patterns of relationship in covert (illegal) networks is very useful for law enforcement agencies and intelligence analysts to investigate collaborations among criminals. Previous studies in network analysis have mostly dealt with overt (legal) networks with transparent structures. Unlike conventional data mining that extracts patterns based on individual data objects, network structure mining is especially suitable for mining a large volume of association data to discover hidden structural patterns in criminal networks. Covert networks share some features with conventional (real world) networks, but they are harder to identify because they mostly hide their illicit activities. After the September 11, 2001 attacks, social network analysis (SNA) has increasingly been used to study criminal networks. However, Finding out who is related to whom on a large scale in a covert network is a complex problem. In this paper we will discuss how network structure mining is applied in the domain of terrorist networks using structural (indices) measures or properties from social network analysis (SNA) and web structural mining research and proposed an algorithm for network disruption. Structural properties are determined by the graph structure of the network. These structural properties are used for locating and isolating core members by using importance ranking score and thereby analyzing the effect to remove these members in terrorist networks. The discussion is supported with a case study of Jemma Islamiah (JI) terrorist network. Key-Words: Networks, Centrality, Dependency, Rank, Influence, and Destabilization.