Predicting Violent Victimization Using Social Network Analysis From Police Data

Extant research suggests that membership in crime networks explains vulnerability to violent crime victimization. Consequently, identifying deviant social networks and understanding their structure and individual members' role in them could provide insight into victimization risk. Identifying social networks may help tailor crime prevention strategies to mitigate victimization risks and dismantle deviant networks. Social network analysis (SNA) offers a particular means of comprehending and measuring such group-level structures and the roles that individuals play within them. When applied to research on crime and victimization, it could provide a foundation for developing precise, effective prevention, intervention, and suppression strategies. This study uses police data to examine whether individuals most central to a deviant social network are those who are most likely to become victims of violent crime, and which crime network roles are most likely to be associated with vulnerability to violent victimization. SNA of these data indicates that network individuals who are in a position to manage the flow of information in the network (betweenness centrality), regardless of their number of connections (degree centrality), are significantly more likely to be homicide and aggravated assault victims. Implications for police practice are discussed.