Crime, Law and Social Change

, Volume 57, Issue 2, pp 151–176 | Cite as

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

  • David A. BrightEmail author
  • Caitlin E. 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.


Methamphetamine Betweenness Centrality Network Member Criminal Group Closeness Centrality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work forms part of the Drug Policy Modelling Program funded by the Colonial Foundation Trust. This paper is based on a paper presented at the Illicit Networks Workshop which was held by the Centre for Transnational Crime Prevention, Wollongong University, Australia (December, 2009). We would like to thank Professor Carlo Morselli for his advice and encouragement.


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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • David A. Bright
    • 1
    • 2
    Email author
  • Caitlin E. Hughes
    • 3
  • Jenny Chalmers
    • 3
  1. 1.Drug Policy Modelling ProgramUniversity of NSWSydneyAustralia
  2. 2.Drug Policy Modelling Program, National Drug and Alcohol Research CentreUNSWSydneyAustralia
  3. 3.Drug Policy Modelling Program, National Drug and Alcohol Research CentreUniversity of NSWSydneyAustralia

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