Social networks community detection using the Shapley value

By increasing the popularity of social networking websites like Facebook and Twitter, analysis of the structure of these networks receives significant attentions. The most important part of these analyses is towards detecting communities. The aforementioned structures are known with extremely high inter-connections versus few intra-connections in the graphs. In this paper, we have addressed the community detection problem by a novel framework based on Information Diffusion Model and Shapley Value Concept. Here, each node of the underlying graph is attributed to a rational agent trying to maximize its Shapley Value in the form of information it receives. Nash equilibrium of the game corresponds to the community structure of the graph. Compared with other methods, our approach demonstrates promising results on the well-known real world and synthetic graphs.