Effective community division based on improved spectral clustering

Abstract

Abstract Not only does attribute of nodes affect the effectiveness and efficiency of community division, but also the relationship of them has a great impact on it. Clusters of arbitrary shape can be identified by the Spectral Clustering (SC). However, k-means clustering used in SC still could result in local optima, and the parameters in Radial Basis Function need to be determined by trial and error. In order to make such algorithm better fit into community division of social network, we try to merge attribute and relationship of node and optimize the ability of spectral clustering to get the global solution, thus a new community clustering algorithm called Spectral Clustering Based on Simulated Annealing and Particle swarm optimization (SCBSP) is proposed. The proposed algorithm is adapted to social networking division. In related experiments, the proposed algorithm, which enhances the global searching ability, has better global convergence and makes better performance in community division than original spectral clustering.