Abstract: In complex network analysis,mining community structure is an important and challenging research topic.The existing deep learning-based methods have achieved good results in graph-related tasks,but they rarely deal with community detection tasks,especially overlapping community detection,and fail to fully mine and utilize network topology information.In response to these problems,this paper proposes an overlapping community detection with graph regularized fuzzy autoencode(FAE).Firstly,an autoencoder is employed to encode the network topology into a low-dimensional representation.This is followed by applying fuzzy C-means clustering to generate a fuzzy membership matrix,which is then decoded to reconstruct the network topology.Next,a graph regularization term－designed to characterize structural information within the network－is integrated into the aforementioned autoencoder framework.Subsequently,the autoencoder architecture with the graph regularization forms a stacked autoencoder to derive a deep fuzzy membership matrix.Finally,based on fuzzy set theory,the deep fuzzy membership matrix is utilized to partition overlapping communities.Experimental results on 3 groups of artificial networks and 6 real networks show that the performance of the proposed method evaluates by overlapping normalized mutual information(ONMI),Jaccard index(Jaccard) and F1-Score is superior to that of most of the 7 classical methods,demonstrating its potential in dealing with overlapping community detection problems.

Key words: Overlapping community discovery, Stacked autoencoder, Fuzzy C-means, Graph regularization, Deep fuzzy membership