Community Detection in Social Networks Using Information Diffusion

Authors:
Alireza Hajibagheri;Hamidreza Alvari;Ali Hamzeh;Sattar Hashemi
Affiliations:
-;-;-;-
Venue:
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Year:
2012

Citing 5
Cited 2

Self-Organization and Identification of Web Communities

Computer
Detecting functional modules in the yeast protein--protein interaction network

Bioinformatics
Relational learning via latent social dimensions

Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Empirical comparison of algorithms for network community detection

Proceedings of the 19th international conference on World wide web
Detecting overlapping communities in social networks by game theory and structural equivalence concept

AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II

Modeling information diffusion and community membership using stochastic optimization

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Finding overlapping communities in a complex network of social linkages and Internet of things

The Journal of Supercomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.