Illuminating dark networks: a social network analysis of an Australian drug trafficking syndicate

 David A. Bright, Caitlin Hughes & Jenny Chalmers

A small but growing number of analysts of criminal activity have used social network analysis (SNA) to characterise criminal organisations and produce valuable insights into the operation of illicit markets. The successful conduct of SNA requires data that informs about links or relationships between pairs of individuals within the group. To date analyses have been undertaken with data extracted from offender databases, transcripts of physical or electronic

surveillance, written summaries of police interrogations, and transcripts of court proceedings. These data can be expensive, time-consuming and complicated to access and analyse. This paper presents findings from a study which aimed to determine the feasibility and utility of conducting SNA using a novel source of data: judges’ sentencing comments. Free of charge, publically accessible without the need for ethics clearance, available at the completion of sentencing and summary in nature, this data offers a more accessible and less expensive alternative to the usual forms of data used. The judges’ sentencing comments were drawn from a series of Australian court cases involving members of a criminal group involved in the manufacture and distribution of methamphetamine during the 1990s. Feasibility is evaluated in terms of the ability to produce a network map and generate the types of quantitative measures produced in studies using alternate data sources. The utility of the findings is judged in relation to the insights they provide into the structure and operation of criminal groups in Australia’s methamphetamine market.   


Dismantling criminal networks: can node attributes play a role?

David A. Bright, Catherine Greenhill, and Natalya Levenkova

Internationally, there is recognition of the need to more clearly understand drug markets and the criminal syndicates groups that operate within them, in order to target drug law enforcement interventions in the most effective ways.  The current project aims to fill some of the gaps in knowledge about the structure of drug trafficking networks using Social Network Analaysis (SNA), and to evaluate the impact of different types of law enforcement interventions directed at drug trafficking networks.  We build on earlier work in which judges’ sentencing comments were used to build a network map of a drug trafficking syndicate which operated in Australia in the 1990s (Bright, Hughes, & Chalmers, 2012).  As well as producing a network map, this study was also able to identify the role that each individual played within the syndicate.  We wish to explore the effectiveness of different hypothetical intervention strategies that aim to dismantle the network. First we investigate the structure of the network and show that it shares some properties of scale-free networks.   Then, four enforcement scenarios will be tested via simulation: (1) interventions which target individuals based on degree centrality; (2) interventions which target individuals based on role, (3) interventions which combine the first two strategies, and (4) random intervention.  The results offer some guidance to intelligence and operational law enforcement when determining which individuals to target, and specifically the impact of targeting individuals based on high degree centrality and roles within the networks, as compared with a baseline (random) intervention.



Evolution of a drug trafficking network: Mapping changes in network structure and function across time

David A. Bright and Jordan Delaney

There is a growing body of research using SNA to study criminal networks.  The great majority of this research examines networks at a single time point.  Although there are theoretical approaches which hypothesize on how criminal networks develop and grow, little empirical research has been conducted on the growth of criminal networks over time.  This project documents the growth of a drug trafficking network.  The aims were to examine and describe structural and functional changes in a criminal network across time. We found that the density of the network remained somewhat stable over time, although the network became more decentralized at the final time point measured.  Centrality scores for individual nodes showed significant changes over time.  Individuals changed the roles performed across time, consistent with the changing needs and focus of the network.  Overall, our results support the characterization of networks as flexible and adaptive.