Higher-Order Community Detection by Motif-Based Modularity Optimization

Recently higher-order community detection based on network motifs has received increasing attention, because motif-based communities reflect not only mesoscale structures but also functional characteristics of real-life networks. In this study, we propose a Modularity Optimization method for Motif-based Community Detection (MOMCD). In order to approximate the global optimum in modularity optimization, an improved nature-inspired metaheuristic algorithm is proposed as optimization strategy. In addition, by comprehensively utilizing motif-based (higher-order) and edge-based (lower-order) structural information, a neighbor community modification operation and a local search operation are also designed to improve the quality of individuals and promote the convergence of MOMCD. Experimental results show that MOMCD is promising and competitive in identifying motif-based communities from synthetic and real-life networks, which outperforms state-of-the-art approaches in terms of quality and accuracy, and deepens our understanding of network structural and functional characteristics.