Would I regret later joining this Community ? Using temporal neighborhood information for community retention in a game theoretic community detection framework

Prateek Chanda, Susanta Chakraborty

Keywords: game theory, temporality, fast convergence, modularity, probabilistic sampling, community retention

Abstract: Detection of relevant and meaningful communities from any social network is always a constant research challenge. Considering large social networks existing approaches often fail to capture perfect community partition or fail to converge fast. Inspired by nonmyopic reinforcement learning domain, in this paper, we have proposed a novel game theory based community detection algorithm which considers community retention based on temporal information as part of a node’s strategy (i.e. for a node whether their current assigned community is more profitable based upon future possibilities, or should they switch at the moment), thereby minimizing cross-community false node switches. The proposed method has the following properties (a) considers community retention for a node in the network when it considers switching to another community, (b) achieves significantly better/comparable performance w.r.t baselines in terms of metrics such as quality of partition on various real world datasets,(c) faster convergence w.r.t traditional game theoretic approach which only considers short-term utility by minimizing cross-community node switches (d) utility to interpret at each iteration for any particular node’s strategy.