Computational & Mathematical Organization Theory
dg.o '05 Proceedings of the 2005 national conference on Digital government research
Proceedings of the 15th international conference on World Wide Web
ACM Transactions on the Web (TWEB)
Communications of the ACM
Using importance flooding to identify interesting networks of criminal activity
Journal of the American Society for Information Science and Technology
The Automatic Identification and Prioritisation of Criminal Networks from Police Crime Data
EuroISI '08 Proceedings of the 1st European Conference on Intelligence and Security Informatics
CrimeWalker: a recommendation model for suspect investigation
Proceedings of the fifth ACM conference on Recommender systems
Using importance flooding to identify interesting networks of criminal activity
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
Collusion set detection through outlier discovery
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
Analyzing terrorist networks: a case study of the global salafi jihad network
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
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Knowledge about criminal networks has important implications for crime investigation and the anti-terrorism campaign. However, lack of advanced, automated techniques has limited law enforcement and intelligence agencies' ability to combat crime by discovering structural patterns in criminal networks. In this research we used the concept space approach, clustering technology, social network analysis measures and approaches, and multidimensional scaling methods for automatic extraction, analysis, and visualization of criminal networks and their structural patterns. We conducted a case study with crime investigators from the Tucson Police Department. They validated the structural patterns discovered from gang and narcotics criminal enterprises. The results showed that the approaches we proposed could detect subgroups, central members, and between-group interaction patterns correctly most of the time. Moreover, our system could extract the overall structure for a network that might be useful in the development of effective disruptive strategies for criminal networks.