title: A community detection algorithm based on topology potential and spectral clustering.
abstract: Community detection is of great value for complex networks in understanding their inherent law and predicting their behavior. Spectral clustering algorithms have been successfully applied in community detection. This kind of methods has two inadequacies: one is that the input matrixes they used cannot provide sufficient structural information for community detection and the other is that they cannot necessarily derive the proper community number from the ladder distribution of eigenvector elements. In order to solve these problems, this paper puts forward a novel community detection algorithm based on topology potential and spectral clustering. The new algorithm constructs the normalized Laplacian matrix with nodes' topology potential, which contains rich structural information of the network. In addition, the new algorithm can automatically get the optimal community number from the local maximum potential nodes. Experiments results showed that the new algorithm gave excellent performance on artificial networks and real world networks and outperforms other community detection methods.
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.
authors.forename: Zhixiao
authors.initials: Z
authors.lastname: Wang
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.
authors.forename: Zhaotong
authors.initials: Z
authors.lastname: Chen
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.
authors.forename: Ya
authors.initials: Y
authors.lastname: Zhao
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.
authors.forename: Shaoda
authors.initials: S
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D008962:Models, Theoretical