On network-aware clustering of Web clients
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
A Graph Based Approach to Extract a Neighborhood Customer Community for Collaborative Filtering
DNIS '02 Proceedings of the Second International Workshop on Databases in Networked Information Systems
Mining scale-free networks using geodesic clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithmic Game Theory
An Algorithm to Find Overlapping Community Structure in Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
A Fast Algorithm to Find Overlapping Communities in Networks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Dynamical Processes on Complex Networks
Dynamical Processes on Complex Networks
Relational learning via latent social dimensions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A game-theoretic framework to identify overlapping communities in social networks
Data Mining and Knowledge Discovery
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Identifying communities in social networks has been receiving the increasing attentions recently. However, the overlapping concept has received little attentions in the literature, although it is observed in almost all social networks. In this study, we propose a framework based on the game theory and the structural equivalence concept to address the detection of overlapping communities in social networks. Specifically, we consider the underlying graph as a hypothetical social networking website and regard each vertex of this graph as an agent performing in this multiagent environment. Since each agent may belong to several communities simultaneously, we are able to find overlapping community structure of social networks. The rigorous proof of the existence of Nash equilibrium in this game is provided which shows that the method always reaches to the final solution. Experimental results on the benchmark and real world graphs show superiority of our approach over the other state-of-the-art methods.