Fast Community Detection Based on Integration of Non-cooperative and Cooperative Game

A clear game process helps to track community generation and evolution, so non-cooperative and cooperative games are applied to community detection. However, non-cooperative games focus on the competition between nodes, disregarding their cooperation. Solely considering individual perspectives often results in insufficient precision. Cooperative games consider the interests of both coalitions and individual. Nevertheless, involving a large number of participants in cooperative games can lead to high computational complexity and slow convergence. In this study, a fast community detection model called FCDG is proposed. It combines non-cooperative and cooperative games by exploring candidate communities and optimizing community merging. Firstly, a intimate core group identification strategy based on node mutual intimacy is designed to accelerate the convergence of candidate community detection using non-cooperative games and maximize individual benefits. Secondly, building upon of candidate community, a candidate community merging approach based on cooperative games is devised to achieve community optimal solution. The performance of FCDG is evaluated on both real-world and synthetic datasets. Experimental results demonstrate that FCDG effectively discovers community structure with higher accuracy and robustness compared to other baseline algorithms.