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2013 IEEE 16th International Conference on Computational Science and Engineering (2009)
Vancouver, Canada
Aug. 29, 2009 to Aug. 31, 2009
ISBN: 978-0-7695-3823-5
pp: 582-587
Abstract—The hidden knowledge in social networks data can beregarded as an important resource for criminal investigations which can help finding the structure and organization of a criminal network. However such network based analysis has not been studied in an applied way and remains mostly a manual process. To assist inspectors and intelligence agencies discover this knowledge, we defined a new problem and then proposed a framework for automated network data analysis and deductionapproach from multiple social networks by converting totransaction dataset, applying association mining, and statistical methods. By applying a game theory concept in a multi-agent model, we try to design a policy for knowledge discovery and inference fusion. This approach enables police stations to build and deploy P2P applications through a unified medium for finding criminals relationship and identifying suspicious guys.
Social Network Analysis, Criminal Network Discovery, Associacion Rule Mining, Multi-agent System
Martin Ester, Amin Milani Fard, "Collaborative Mining in Multiple Social Networks Data for Criminal Group Discovery", 2013 IEEE 16th International Conference on Computational Science and Engineering, vol. 04, no. , pp. 582-587, 2009, doi:10.1109/CSE.2009.435
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