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

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

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Article ID: iaor20051058
Country: Netherlands
Volume: 38
Issue: 3
Start Page Number: 473
End Page Number: 487
Publication Date: Dec 2004
Journal: Decision Support Systems
Authors: ,
Keywords: law & law enforcement
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.

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