Return to search

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

Artificial Intelligence Lab, Department of MIS, University of Arizona / 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 investigatorsâ 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 algorithmâ 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.

Identiferoai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/106207
Date January 2004
CreatorsXu, Jennifer J., Chen, Hsinchun
PublisherElsevier
Source SetsUniversity of Arizona
LanguageEnglish
Detected LanguageEnglish
TypeJournal Article (Paginated)

Page generated in 0.0028 seconds