An improved random walk based community detection algorithm

Abstract

Community detection is an important issue in social network analysis, which aims at finding potential community structures such that the internal nodes of a community have higher closeness than external nodes. Taking into account node attribute information, this paper presents an improved community detection algorithm based on random walk. Based on the basic understanding that people getting together often relies on their common interests, node similarities are initially calculated with node attributes and iteratively updated based on the random walk model. Meanwhile, node importance is computed to represent how much it can influence other nodes, based on which some important nodes are selected as seeds for community clustering. As for overlapping community detection, some construction is made on a given social network. Experimental results on several real datasets show our approach has better effects than previous methods on both overlapping and non- overlapping communities. © 2014 SERSC.