An effective and scalable overlapping community detection approach: Integrating social identity model and game theory

Because of its broad real-life application, community detection (in the realm of a complex network) is an attractive challenge to many researchers. However, current methods fail to reveal the full community structure and its formation process. Thus, here we present SIMGT, an effective and Scalable approach that detects overlapping communities: Integrating social identity Model and Game Theory. Inspired by social identity theory and nodes' high-order proximities, first we weight and rewire the original network, then we associate each node with a new utility function. Next, we model community formation as a non-cooperative game among all nodes, and we regard the nodes as self-interested players. Further, we use a stochastic gradient-ascent method to update players' strategies toward different communities, and prove that our game greatly resembles and matches how a potential game works (in the classical sense in game theory), indicating that the Nash equilibrium point must exist. Finally, we implement comprehensive experiments on several synthetic and real-life networks. The results show that whatever weighting strategy we choose, SIMGT can gain better performance on community detection task. In particular, SIMGT achieves a best result when we choose the Jaccard coefficient. After comparing SIMGT with six benchmark algorithms, we obtain convincing results in terms of how well the algorithms reveal communities, as well as algorithms' scalability. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

The SIMGT approach, integrating social identity model and game theory, effectively detects overlapping communities in complex networks, revealing community structures accurately. By incorporating weighting, rewiring, nodes' utility functions, and non-cooperative games, SIMGT outperforms benchmark algorithms and demonstrates scalability, especially when utilizing the Jaccard coefficient for community detection.