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Networks are characterized by participants with varying interests, qualifications and behaviors. The existing methods and algorithms used for community formation in these networks fail to capture the behavioral aspect of the users who form the community. In this paper, we introduce a game-theoretic approach to address the community formation problem in social networks based on Knowledge Quotient [KQ]. Given an underlying social graph, we assume that each node is a selfish agent who wishes to be in a community where his peers are of the same KQ. We formulate the agents utility based on the modularity concept introduced by Newman  and extending it to include the KQ criteria which helps in forming communities with the same behavior. The proposed paper helps address social network problems like diffusion of information and innovation, e-commerce (marketing), job finding, build effective social and political campaigns in a better way by considering the KQ of the members of the social community.