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Community Detection in Social Networks Using Information Diffusion

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4 Author(s)
Hajibagheri, A. ; Comput. Sci. & Eng. Dept., Shiraz Univ., Shiraz, Iran ; Alvari, H. ; Hamzeh, A. ; Hashemi, S.

Discovering communities in popular social networks like Facebook has been receiving significant attentions recently. In this paper, inspired from real life, we have addressed the community detection problem by a framework based on Information Diffusion Model and Game Theory. In this approach, we consider each node of the social network as a selfish agent which has interactions with its neighbors and tries to maximize its total utility (i.e. received information). Finally community structure of the graph reveals after reaching to the local Nash equilibrium of the game. Experimental results on the benchmark social media datasets, synthetic and real world graphs demonstrate that our method is superior compared with the other state-of-the-art methods.

Published in:

Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on

Date of Conference:

26-29 Aug. 2012