Fuzzy Community Detection Based on Elite Symbiotic Organisms Search and Node Neighborhood Information

Abstract

In the last decade, fuzzy community detection has received increasing attention, since it cannot only uncover the community structure of a network, but also reflect the membership degrees of each node to multiple communities. Although some pioneers proposed a few algorithms for finding fuzzy communities, there is still room for further improvement in the quality of detected fuzzy communities. In this study, a metaheuristic-based modularity optimization algorithm, named symbiotic organisms search fuzzy community detection (SOSFCD) is proposed. On the one hand, an improved bioinspired metaheuristic algorithm, elite symbiotic organisms search, is designed as optimization strategy to improve the global convergence of fuzzy modularity optimization. On the other hand, a neighbor-based membership modification operation is proposed to intensify exploitation ability and speed up convergence, by efficiently utilizing local information (i.e., node neighborhood) of network topology. Experimental results on both of synthetic and real-life networks with different scales and characteristics show that SOSFCD can find max-modularity fuzzy partitions and coverings, and outperforms many state-of-the-art algorithms in terms of accuracy and stability.