Community detection game
Real-world networks are often cluttered and hard to organize. Recent studies show that most networks have the community structure, i.e., nodes with similar attributes form a certain community, which enables people to better understand the constitution of the networks. Hitherto, various community detection methods have been proposed in the literature yet none of them takes the strategic interactions among nodes into consideration. Additionally, many real-world observations of networks are noisy and incomplete, i.e., with some missing links or fake links, due to either technology constraints or privacy regulations. In this work, a game-theoretic framework of community detection is established, where nodes interact and produce links with each other in a rational way based on mutual benefits. Given the proposed game-theoretic generative models for communities, we use expectation maximization (EM) algorithm to detect communities. Simulations on synthetic networks and experiments on real-world networks demonstrate that the proposed detection method outperforms the state-of-the-art.