Evolutionary multiobjective overlapping community detection based on similarity matrix and node correction

Abstract

The method of overlapping community detection based on fuzzy clustering is sensitive to the initialization of community centers, which easily traps in local optima and leads to node misclassification. This paper proposes an evolutionary multiobjective algorithm based on similarity matrix and node correction to detect overlapping communities to solve the above problems. Firstly, the algorithm determines a similarity community for each node by setting the similarity threshold. Then, the central nodes are found more accurately through the similarity distribution of the similarity communities. Secondly, under the framework of the evolutionary multiobjective algorithm, the similarity communities of the central nodes are used as the initial communities to obtain the nonoverlapping communities. In addition, the algorithm proposes a correction strategy for the noncentral nodes based on the similarity communities. The correction strategy obtains the adjacent nodes of each node’s similarity community. It then uses each adjacent node’s community to correct the nonoverlapping community. Finally, the algorithm adjusts the noncentral nodes’ correction strategy. This correction strategy corrects the overlapping nodes according to the number of each overlapping node’s labels. It takes the separation operation to further correct overlapping nodes to obtain the corrected overlapping communities. This paper uses seventeen real networks and a variety of synthetic networks with different parameters to verify the proposed algorithm’s effectiveness. And the proposed algorithm achieves higher accuracy of community detection in most networks than four state-of-the-art overlapping community detection algorithms.