Untangling criminal networks: A Case study

Jennifer Xu, Hsinchun Chen

Research output: Chapter in Book/Report/Conference proceedingChapter

27 Scopus citations

Abstract

Knowledge about criminal networks has important implications for crime investigation and the anti-terrorism campaign. However, lack of advanced, automated techniques has limited law enforcement and intelligence agencies' ability to combat crime by discovering structural patterns in criminal networks. In this research we used the concept space approach, clustering technology, social network analysis measures and approaches, and multidimensional scaling methods for automatic extraction, analysis, and visualization of criminal networks and their structural patterns. We conducted a case study with crime investigators from the Tucson Police Department. They validated the structural patterns discovered from gang and narcotics criminal enterprises. The results showed that the approaches we proposed could detect subgroups, central members, and between-group interaction patterns correctly most of the time. Moreover, our system could extract the overall structure for a network that might be useful in the development of effective disruptive strategies for criminal networks.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsHsinchun Chen, Daniel D. Zeng, Therani Madhusudan, Richard Miranda, Jenny Schroeder, Chris Demchak
PublisherSpringer-Verlag
Pages232-248
Number of pages17
ISBN (Print)354040189X, 9783540401896
DOIs
StatePublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2665
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Xu, J., & Chen, H. (2003). Untangling criminal networks: A Case study. In H. Chen, D. D. Zeng, T. Madhusudan, R. Miranda, J. Schroeder, & C. Demchak (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 232-248). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2665). Springer-Verlag. https://doi.org/10.1007/3-540-44853-5_18