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Hiding in plain sight: criminal network analysis

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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.

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Abbreviations

SNA:

Social Network Analysis;

DNA:

Dynamic Network Analysis;

CASOS:

Center for Computational Analysis of Social and Organizational Systems;

HIDTASIS:

High intensity drug trafficking area strategic information system;

LEA:

Law enforcement agency

References

  • Boissevain J (1974) Friends of friends: networks, manipulators and coalitions. Blackwell & Mott, Bristol

    Google Scholar 

  • Carley K, Reminga J (2004) ORA: organization risk analyzer (Technical Report No. CMU-ISRI-04-106). Carnegie Mellon University, School of Computer Science, Pittsburgh

    Google Scholar 

  • Carley KM, Diesner J, Reminga J, Tsvetovat M (2007) Toward an interoperable dynamic network analysis toolkit. Decis Support Syst 43(4):1324–1347

    Article  Google Scholar 

  • Coles N (2001) It’s not what you know it’s who you know that counts. Analyzing serious crime groups as social networks. Br J Criminol 41(4):580

    Article  Google Scholar 

  • Diesner J, Carley K (2004) AutoMap 1.2—Extract, analyze, represent, and compare mental models from texts. (No. CMU-ISRI-04-100). Carnegie Mellon University, School of Computer Science, Institute for Software Research International, Pittsburg

    Google Scholar 

  • Diesner J, Carley K (2005) Revealing social structure from text: meta-matrix text analysis as a novel method for network text analysis. In: Narayanan VK, Armstrong DJ (eds) Causal semantic networking for information systems and technology research: approaches, advances, and illustrations. Idea Group, Harrisburg, pp 81–108

    Google Scholar 

  • Diesner J, Carley K (2008) Conditional random fields for entity extraction and ontological text coding. Comput Math Organ Theory 14:248–262

    Article  Google Scholar 

  • Innes M, Fielding N, Cope N (2005) “The appliance of science”? The theory and practice of crime intelligence analysis. Br J Criminol 45(1):39–57

    Article  Google Scholar 

  • Klerks P (2001) The network paradigm applied to criminal organizations: theoretical, nitpicking, or a relevant doctrine for investigators? Recent developments from the Netherlands. Connections 24(3):53–65

    Google Scholar 

  • McGloin J (2005) Street gangs and interventions: innovative problem solving with network analysis. Department of Justice, Office of Community Oriented Policing Services, Washington

    Google Scholar 

  • Ozgul F, Bondy J, Aksoy H (2007) Mining for offender group detection and story of a police operation. Paper presented at the Proceedings of the sixth Australasian conference on data mining and analytics - Vol 70. From http://portal.acm.org/citation.cfm?id=1378245.1378270#

  • Ressler S (2006) Social network analysis as an approach to combat terrorism: past, present, and future research. Homel Secur Aff 2(2):1–10

    Google Scholar 

  • Stewart TA (2001) Six degrees of Mohamed Atta. Business 2.0 2(10):63

    Google Scholar 

  • Williams P (2001) Transnational criminal networks. In: Arquilla J, Ronfeldt D (eds) Networks and netwars: the future of terror, crime, and militancy. Rand, Santa Monica, pp 61–97

    Google Scholar 

  • Xu J, Chen H (2005) Criminal network analysis and visualization. Commun ACM 48(6):100

    Article  Google Scholar 

  • Xu JJ, Hsinchun C (2005) CrimeNet explorer: a framework for criminal network knowledge discovery. ACM Trans Inf Syst 23(2):201

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. North Texas High Intensity Drug Trafficking Area (NTHIDTA), Irving, TX, USA

    Christopher E. Hutchins

  2. School of Nursing, Bouvé College of Health Sciences, Northeastern University, 360 Huntington Ave, Boston, MA, 02115, USA

    Marge Benham-Hutchins

Authors
  1. Christopher E. Hutchins
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  2. Marge Benham-Hutchins
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Correspondence to Marge Benham-Hutchins.

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Hutchins, C.E., Benham-Hutchins, M. Hiding in plain sight: criminal network analysis. Comput Math Organ Theory 16, 89–111 (2010). https://doi.org/10.1007/s10588-009-9060-8

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  • DOI: https://doi.org/10.1007/s10588-009-9060-8

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