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Hiding in plain sight: criminal network analysis

Author

Listed:
  • Christopher E. Hutchins

    (North Texas High Intensity Drug Trafficking Area (NTHIDTA))

  • Marge Benham-Hutchins

    (Northeastern University)

Abstract

The United States is faced with an increasingly complex criminal enterprise. Advances in technology, communications, transport, and economies enable a highly adaptive criminal element to hide in plain site. These advances provide criminal organizations with the same global boundaries and opportunities as legitimate organizations. As boundaries expand the data to be analyzed by law enforcement mounts at a geometrically astounding rate. In response, the nature of law enforcement intelligence analysis must evolve to cope with both the amount and complexity of the data. This requires new and adaptive methods of analysis. Researchers have found that the principles of network analysis can be applied to the analysis of terrorist and criminal organizations. This paper examines the combination of measures historically employed by intelligence analysts and network analysis software and methodologies to quantitatively and qualitatively examine criminal organizations.

Suggested Citation

  • Christopher E. Hutchins & Marge Benham-Hutchins, 2010. "Hiding in plain sight: criminal network analysis," Computational and Mathematical Organization Theory, Springer, vol. 16(1), pages 89-111, March.
    • Handle: RePEc:spr:comaot:v:16:y:2010:i:1:d:10.1007_s10588-009-9060-8
    DOI: 10.1007/s10588-009-9060-8
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    References listed on IDEAS

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    1. Jana Diesner & Kathleen M. Carley, 2008. "Conditional random fields for entity extraction and ontological text coding," Computational and Mathematical Organization Theory, Springer, vol. 14(3), pages 248-262, September.
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    Cited by:

    1. Fan, Changjun & Liu, Zhong & Lu, Xin & Xiu, Baoxin & Chen, Qing, 2017. "An efficient link prediction index for complex military organization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 572-587.
    2. Hanning Yuan & Yanni Han & Ning Cai & Wei An, 2018. "A Multi-Granularity Backbone Network Extraction Method Based on the Topology Potential," Complexity, Hindawi, vol. 2018, pages 1-8, October.
    3. Sasson, Elan & Ravid, Gilad & Pliskin, Nava, 2015. "Improving similarity measures of relatedness proximity: Toward augmented concept maps," Journal of Informetrics, Elsevier, vol. 9(3), pages 618-628.
    4. Annamaria Ficara & Francesco Curreri & Giacomo Fiumara & Pasquale De Meo & Antonio Liotta, 2022. "Covert Network Construction, Disruption, and Resilience: A Survey," Mathematics, MDPI, vol. 10(16), pages 1-43, August.

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