Overlapping Community Detection Algorithm Based on Two-Stage Clustering
JIANG Sheng-Yi1, YANG Bo-Hong1, LI Min-Min1, WU Mei-Ling2, WANG Lian-Xi3
1.Cisco School of Informatics, Guangdong University of Foreign Studies, Guangzhou 510006 2.Taobao(China) Software Co., Ltd, Hangzhou 310099 3.Library, Guangdong University of Foreign Studies, Guangzhou 510420
Abstract Aiming at the complex network overlapping community detection, an overlapping community detection algorithm based on two-stage clustering is proposed. Eigen decomposition is applied to network adjacency matrix. The nodes are projected into k-dimensional Euclidean space, and then they are clustered by hard and soft clustering algorithm to detect the structure of overlapping community efficiently and adaptively. At the stage of hard clustering, a clustering algorithm based on the principle of minimum distance is introduced to divide nodes autonomously, and the number of communities and cluster centers for the soft clustering stage are determined. At the stage of soft clustering, fuzzy C-means algorithm is introduced and the fuzzy modularity is considered as objective function for the algorithm. Through iterative optimization of the fuzzy modularity, a soft partition is realized and overlapping community structures in network can be figured out. Experiments are carried out on a number of real network datasets, and the results indicate that the proposed algorithm can mine overlapping community structure in complex network with high efficiency and effectiveness.
2015, Vol. 28 
