Association and Centrality in Criminal Networks

Abstract

Network-based techniques are widely used in criminal investigations because patterns of association are actionable and understandable. Existing network models with nodes as first class entities and their related measures (e.g., social networks and centrality measures) are unable to capture and analyze the structural richness required to model and investigate criminal network entities and their associations. We demonstrate a need to rethink entity associations with one specific case (inspired by The Wire, a tv series about organized crime in Baltimore, United States) and corroborated by similar evidence from other cases. Our goal is to develop centrality measures for fragmented and non-navigational states of criminal network investigations. A network model with three basic first class entities is presented together with a topology of associations between network entities. We implement three of these associations and extend and test two centrality measures using CrimeFighter Investigator, a novel tool for criminal network investigation. Our findings show that the extended centrality measures offer new insights into criminal networks.