SOCIAL NETWORKS COMMUNITY DETECTION USING THE SHAPLEY VALUE

As a result of the increasing popularity of social networking websites like Facebookand Twitter, analysis of the structure of these networks has received significant attention. The mostimportant part of these analyses is towards detecting communities. The aforementioned structuresare usually known with extremely high inter-connections versus few intra-connections in thegraphs. In this paper, in spite of most approaches being optimization based, we have addressed thecommunity detection problem (CDP) by a novel framework based on Information DiffusionModel and Shapley Value Concept. Here, each node of the underlying graph is attributed to arational agent trying to maximize its Shapley Value in the form of information it receives. Nashequilibrium of the game corresponds to the community structure of the graph. Compared with theother methods, our approach demonstrates promising results on the well-known real world andsynthetic graphs.