TY - JOUR
T1 - Fighting organized crimes
T2 - Using shortest-path algorithms to identify associations in criminal networks
AU - Xu, Jennifer J.
AU - Chen, Hsinchun
N1 - Funding Information:
This project has primarily been funded by the National Science Foundation, Digital Government Program, “COPLINK Center: Information and Knowledge Management for Law Enforcement,” #9983304, July 2000–June 2003. We would like to thank the following people for their support and assistance during the entire project development and evaluation process: Dr. Homa Atabakhsh, Michael Chau, JoAnna Davis, and other members of the University of Arizona Artificial Intelligence Lab, Detective Tim Petersen, and other personnel from the Tucson Police Department and the Phoenix Police Department.
PY - 2004/12
Y1 - 2004/12
N2 - 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.
AB - 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.
KW - Concept space
KW - Crime investigation
KW - Law enforcement
KW - Link analysis
KW - Organized crime
KW - Shortest-path algorithm
UR - http://www.scopus.com/inward/record.url?scp=4544224894&partnerID=8YFLogxK
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U2 - 10.1016/S0167-9236(03)00117-9
DO - 10.1016/S0167-9236(03)00117-9
M3 - Article
AN - SCOPUS:4544224894
SN - 0167-9236
VL - 38
SP - 473
EP - 487
JO - Decision Support Systems
JF - Decision Support Systems
IS - 3
ER -