Fighting organized crimes: using shortest-path algorithms to identify associations in criminal networks
| dc.contributor.author | Xu, Jennifer J. | |
| dc.contributor.author | Chen, Hsinchun | |
| dc.date.accessioned | 2004-10-29T00:00:01Z | |
| dc.date.available | 2010-06-18T23:42:33Z | |
| dc.date.issued | 2004 | en_US |
| dc.date.submitted | 2004-10-29 | en_US |
| dc.identifier.citation | Fighting organized crimes: using shortest-path algorithms to identify associations in criminal networks 2004, 38(3):473-487 Decision Support Systems | en_US |
| dc.identifier.uri | http://hdl.handle.net/10150/106207 | |
| dc.description | Artificial Intelligence Lab, Department of MIS, University of Arizona | en_US |
| dc.description.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. | |
| dc.format.mimetype | application/pdf | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier | en_US |
| dc.subject | National Science Digital Library | en_US |
| dc.subject | NSDL | en_US |
| dc.subject | Artificial intelligence lab | en_US |
| dc.subject | AI lab | en_US |
| dc.subject | Artificial Intelligence | en_US |
| dc.title | Fighting organized crimes: using shortest-path algorithms to identify associations in criminal networks | en_US |
| dc.type | Journal Article (Paginated) | en_US |
| dc.identifier.journal | Decision Support Systems | en_US |
| refterms.dateFOA | 2018-08-21T16:47:41Z | |
| html.description.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. |
