Node Local Similarity Based Two-stage Density Peaks Algorithm for Overlapping Community Detection

Abstract

In order to detect overlapping community structures in complex networks,the idea of density peaks clustering algorithm is introduced.However,applying the density peaks clustering algorithm to community detection still has problems such as how to measure the distance between nodes and how to generate overlapping partition results.Therefore,a node local similarity based two-stage density peaks algorithm for overlapping community detection is proposed (LSDPC).By combining hub promoted index and connection contribution degree,a new node local similarity index is defined,and the node distance is measured with the node local similarity.Then the local density and minimum distance of nodes are used to calculate their center values and Chebyshev inequality is used to select communities’ center nodes.The overlapping communities are obtained through initial assignment and overlapping assignment.Experimental results on real network datasets and synthetic network datasets show that the proposed algorithm can effectively detect overlapping community structure,and the results are better than that of other algorithms.

Keywords

• overlapping community detection|density peaks|node similarity|<i>k</i>-nearest neighbors|degree of membership