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Collaborative Mining in Multiple Social Networks Data for Criminal Group Discovery

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2 Author(s)
Fard, Amin Milani ; Sch. of Comput. Sci., Simon Fraser Univ., Burnaby, BC, Canada ; Ester, M.

The hidden knowledge in social networks data can be regarded 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 deduction approach from multiple social networks by converting to transaction 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.

Published in:

Computational Science and Engineering, 2009. CSE '09. International Conference on  (Volume:4 )

Date of Conference:

29-31 Aug. 2009