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

Persistent Link:
http://hdl.handle.net/10150/106207
Title:
Fighting organized crimes: using shortest-path algorithms to identify associations in criminal networks
Author:
Xu, Jennifer J.; Chen, Hsinchun
Citation:
Fighting organized crimes: using shortest-path algorithms to identify associations in criminal networks 2004, 38(3):473-487 Decision Support Systems
Publisher:
Elsevier
Journal:
Decision Support Systems
Issue Date:
2004
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/106207
Submitted date:
2004-10-29
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 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.
Type:
Journal Article (Paginated)
Language:
en
Keywords:
Artificial Intelligence
Local subject classification:
National Science Digital Library; NSDL; Artificial intelligence lab; AI lab

Full metadata record

DC FieldValue Language
dc.contributor.authorXu, Jennifer J.en_US
dc.contributor.authorChen, Hsinchunen_US
dc.date.accessioned2004-10-29T00:00:01Z-
dc.date.available2010-06-18T23:42:33Z-
dc.date.issued2004en_US
dc.date.submitted2004-10-29en_US
dc.identifier.citationFighting organized crimes: using shortest-path algorithms to identify associations in criminal networks 2004, 38(3):473-487 Decision Support Systemsen_US
dc.identifier.urihttp://hdl.handle.net/10150/106207-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractEffective 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.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectArtificial Intelligenceen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.titleFighting organized crimes: using shortest-path algorithms to identify associations in criminal networksen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalDecision Support Systemsen_US
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