A Decomposition Based Multiobjective Evolutionary Algorithm for Dynamic Overlapping Community Detection

Xing Wan, Xingquan Zuo, Feng Song

Abstract: Dynamic and overlapping are common features of community structures of many real world complex networks. There are few studies on detecting dynamic overlapping communities, but those studies consider only single optimization objective. In practice, it is necessary to evaluate the community detection by multiple metrics to reflect different aspects of a community structure. In this paper, we propose a multi-objective evolutionary algorithm based approach for the problem of dynamic overlapping community detection, with consideration of three optimization objectives: partition density (D), the extended modularity (EQ), and the community smoothing (NMILFK). The dynamic overlapping network is regarded as a set of network snapshots. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is used to detect the communities for each snapshot. Experiments show that our approach can find uniformly distributed Pareto solutions for the problem and outperforms those comparative approaches.