Integrating relations and criminal background to identifying key individuals in crime networks

Abstract

One of the most common methods used in the social network analysis of criminal groups is node importance evaluation, which focuses on the links between network members to identify likely crime suspects. Because such traditional node evaluators do not take full advantage of group members' individual criminal propensities, a new evaluator called the social network criminal suspect evaluator (SNCSE) is proposed. SNCSE incorporates members' individual criminal propensities into the node importance evaluation and employs a novel perspective based on concepts of human and social capital, an ego network structure, and an analogy between social interaction and field theory. SNCSE is applied to solve two real-world problems. Its effectiveness is compared with that of traditional evaluators. The results show that integrating criminal propensity into network analysis enables the more accurate identification of key suspects compared to alternative evaluators.