A Community Detection Algorithm Based on the Similarity Sequence

摘要

Community detection is a hot topic in the field of complex social networks. It is of great value to personalized recommendation, protein structure analysis, public opinion analysis, etc. However, most existing algorithms detect communities with misclassified nodes and peripheries, and the clustering accuracy is not high. In this paper, in terms of the agglomerative hierarchical clustering, a community detection algorithm based on the similarity sequence is proposed, named as ACSS (Agglomerative Clustering Algorithm based on the Similarity Sequence). First, similarities of nodes are sorted in descending order to get a sequence. Then pairs of nodes are merged according to the sequence to construct a preliminary community structure. Secondly, the agglomerative clustering process is carried out to get the optimal community structure. The proposed algorithm is tested on real network and computer-generated network data sets. Experimental results show that ACSS can solve the problem of neglecting peripheries. Compared with the existing representative algorithms, it can detect stronger community structure, and improve the clustering accuracy.