Overlapping community detection based on bridging structural features and fuzzy C-means - Ailian Wang; Mingwu Li; Xuyang Gao; Bolin Li; Zhiqiang Su (PLoS ONE, 2025)
In recent years, research on community structure for complex networks has received increasing greater attention, and the overlapping community structure is more closely related to the actual social structure than the non-overlapping community structure, so it is necessary to identify and detect the overlapping communities of social networks. In this paper, we propose an overlapping community optimization method (OSFCM) based on network structure characteristics and fuzzy C-means clustering. We first abstract the feature vector matrix of each node from the network structural properties, and then optimize this matrix by a new objective function gradient optimization method, we generate the preliminary community delineation results with FCM method, and finally calibrate the communities to which the nodes belong. Experimental results show that the algorithm exhibits higher delineation accuracy and better algorithmic performance on seven real network datasets and four synthetic networks.