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Using shortest path algorithms to identify criminal associations
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Dynamic social network analysis using latent space models
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Using importance flooding to identify interesting networks of criminal activity
Journal of the American Society for Information Science and Technology
Using importance flooding to identify interesting networks of criminal activity
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Social network intelligence analysis to combat street gang violence
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Link (association) analysis has been used in law enforcement and intelligence domains to extract and search associations between people from large datasets. Nonetheless, link analysis still faces many challenging problems, such as information overload, high search complexity, and heavy reliance on domain knowledge. To address these challenges and enable crime investigators to conduct automated, effective, and efficient link analysis, we proposed three techniques which include: the concept space approach, a shortest-path algorithm, and a heuristic approach that captures domain knowledge for determining importance of associations. We implemented a system called CrimeLink Explorer based on the proposed techniques. Results from our user study involving ten crime investigators from the Tucson Police Department showed that our system could help subjects conduct link analysis more efficiently. Additionally, subjects concluded that association paths found based on the heuristic approach were more accurate than those found based on the concept space approach.