  Communities typically capture homophily as people of the same community share
many common features. This paper is motivated by the problem of community
detection in social networks, as it can help improve our understanding of the
network topology and the spread of information. Given the selfish nature of
humans to align with like-minded people, we employ game theoretic models and
algorithms to detect communities in this paper. Specifically, we employ
coordination games to represent interactions between individuals in a social
network. We represent the problem of community detection as a graph
coordination game. We provide a novel and scalable two phased approach to
compute an accurate overlapping community structure in the given network. We
evaluate our algorithm against the best existing methods for community
detection and show that our algorithm improves significantly on benchmark
networks (real and synthetic) with respect to standard Normalised Mutual
Information measure.
