关键词: 社会网络, 簇结构, 局部信息, 标签传播, 节点属性相似度

Abstract: In recently years, detecting communities of social networks has made considerable progress, and has been widely applied in the social networks, World Wide Web, biological networks and many other fields. For the hot issues of community detection in social network, firstly the correlate clustering algorithms based on local information are studied and summarized. Secondly, the label propagation algorithm that is short for Label Propagation Algorithm（LPA） is researched and analyzed in-depth, in order to solve the drawback of the algorithm which the network partition is always not optimal due to the random strategy of LPA, this paper introduces the concept of similarity of node attributes, and proposes label propagation algorithm on the basis of the similarity of node attributes called LPA-SNA（Label Propagation Algorithm based on the Similarity of Node Attributes） for short. Finally, taking the American College football network, DBLP co-researchers network as data set, the paper achieves the original label propagation algorithm and improves label propagation algorithm based on the similarity of node attributes respectively, and compares their performance. Experimental results show that the label propagation algorithm based on the similarity of node attributes is more effective compared with the original label propagation algorithm, which not only enhances quality of the network clustering, optimizes the clustering results, but also reduces the time overhead of the algorithm and improves clustering speed.

Key words: social networks, communities, local information, label propagation, similarity of node attributes