Exploring drug-related crimes with social network analysis

Abstract

In recent years, drug abuse and drug addiction have become a major burden to the society. In order to achieve the public expectation for drug crime prevention, law enforcement agencies devote considerable resources hoping to strengthen the intensity of interventions. However, with the rapid changes in social patterns, the drug criminals also look for ways to avoid law enforcement investigations by producing, transporting and selling drugs through different regions, making drug prevention more difficult. Thus, developing dominant strategies to deal with this issue is a main task for police agencies. For more effectively analyze the structural influences of drug crime, we utilize social network analysis (SNA) techniques to discover implications of drug related crime networks. The macro-level perspective of co-offender network indicates that criminals intend to set blocks between network members to prevent law enforcement interventions. The micro-level perspective of individuals provides significant social features to predict drug recidivism. The experimental results indicate superior performance when adopting both personal and social features in classification task. Applying SNA to recidivism prediction is a leading endeavor, and the approach presented in this paper offers remarkable improvement on traditional methods. The results of this paper reveals the advantages of structural implications in analyzing drug related crime, as well as its ability to facilitate the cognition of crime prevention and intervention strategies.