Early Identification of Violent Criminal Gang Members

TL;DR

This paper presents a novel method for early identification of violent gang members using social network analysis and arrest data, achieving high accuracy and outperforming existing law enforcement techniques.

Contribution

Introduces modified centrality measures and a classification approach that effectively predict violent gang members from social network data and arrest metadata.

Findings

Achieves 0.89 precision and 0.78 recall in identifying violent gang members.

Outperforms current law enforcement methods in dynamic network scenarios.

Effective in both complete and evolving social network data environments.

Abstract

Gang violence is a major problem in the United States accounting for a large fraction of homicides and other violent crime. In this paper, we study the problem of early identification of violent gang members. Our approach relies on modified centrality measures that take into account additional data of the individuals in the social network of co-arrestees which together with other arrest metadata provide a rich set of features for a classification algorithm. We show our approach obtains high precision and recall (0.89 and 0.78 respectively) in the case where the entire network is known and out-performs current approaches used by law-enforcement to the problem in the case where the network is discovered overtime by virtue of new arrests - mimicking real-world law-enforcement operations. Operational issues are also discussed as we are preparing to leverage this method in an operational environment.