Abstract: Community discovery and analysis is a hot topic in the study of complex network structures and functions.At present,the widely used algorithm for community partitioning has some problems,such as high time complexity,inaccurate quantification of the number of community cores,and low partitioning accuracy.Therefore,this paper proposed a community detection algorithm ELSC based on local similarity of feature vectors.The algorithm first calculates the eigenvector centrality of each node in the network.On this basis,the eigenvector local similarity (ELS) and eigenvector attractiveness (EA) indicators were proposed.The ELS index indicates the similarity between nodes.To form the initial community,the similarity between the nodes within the same community is higher,and the similarity between different community nodes is lower.The EA index considers the local similarity and the eigenvector centrality ratio,indicating the node.The attraction is used to optimize the initial community and complete the community division of the network.The algorithm determines the node by the most value,avoiding the problem that the threshold number of nodes is uncertain.The modularity and standardized mutual information between the proposed algorithm and six well-known algorithms were compared on seven real networks.Numerical simulation results show that the algorithm has high accuracy and low time complexity.

Key words: Community detection, Eigenvector attractiveness, Eigenvector centrality, Eigenvector local similarity