Fighting organized crimes: Using shortest-path algorithms to identify associations in criminal networks

Jennifer J. Xu, Hsinchun Chen

Research output: Contribution to journalArticle

87 Citations (Scopus)

Abstract

Effective and efficient link analysis techniques are needed to help law enforcement and intelligence agencies fight organized crimes such as narcotics violation, terrorism, and kidnapping. In this paper, we propose a link analysis technique that uses shortest-path algorithms, priority-first-search (PFS) and two-tree PFS, to identify the strongest association paths between entities in a criminal network. To evaluate effectiveness, we compared the PFS algorithms with crime investigators' typical association-search approach, as represented by a modified breadth-first-search (BFS). Our domain expert considered the association paths identified by PFS algorithms to be useful about 70% of the time, whereas the modified BFS algorithm's precision rates were only 30% for a kidnapping network and 16.7% for a narcotics network. Efficiency of the two-tree PFS was better for a small, dense kidnapping network, and the PFS was better for the large, sparse narcotics network.

Original languageEnglish (US)
Pages (from-to)473-487
Number of pages15
JournalDecision Support Systems
Volume38
Issue number3
DOIs
StatePublished - Dec 2004

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Crime
Narcotics
Computer crime
Terrorism
Law enforcement
Law Enforcement
Intelligence
Research Personnel
Shortest path
Organized crime
Organized Crime

Keywords

  • Concept space
  • Crime investigation
  • Law enforcement
  • Link analysis
  • Organized crime
  • Shortest-path algorithm

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Information Systems and Management

Cite this

Fighting organized crimes : Using shortest-path algorithms to identify associations in criminal networks. / Xu, Jennifer J.; Chen, Hsinchun.

In: Decision Support Systems, Vol. 38, No. 3, 12.2004, p. 473-487.

Research output: Contribution to journalArticle

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