title: Uncovering community structures with initialized Bayesian nonnegative matrix factorization.
abstract: Uncovering community structures is important for understanding networks. Currently, several nonnegative matrix factorization algorithms have been proposed for discovering community structure in complex networks. However, these algorithms exhibit some drawbacks, such as unstable results and inefficient running times. In view of the problems, a novel approach that utilizes an initialized Bayesian nonnegative matrix factorization model for determining community membership is proposed. First, based on singular value decomposition, we obtain simple initialized matrix factorizations from approximate decompositions of the complex network's adjacency matrix. Then, within a few iterations, the final matrix factorizations are achieved by the Bayesian nonnegative matrix factorization method with the initialized matrix factorizations. Thus, the network's community structure can be determined by judging the classification of nodes with a final matrix factor. Experimental results show that the proposed method is highly accurate and offers competitive performance to that of the state-of-the-art methods even though it is not designed for the purpose of modularity maximization.
authors.affiliation: School of Computer Science and Technology, Tianjin University, Tianjin, China.
authors.forename: Xianchao
authors.initials: X
authors.lastname: Tang
authors.affiliation: School of Computer Science and Technology, Civil Aviation University of China, Tianjin, China; Information Technology Research Base of Civil Aviation Administration of China, Tianjin, China.
authors.forename: Tao
authors.initials: T
authors.lastname: Xu
authors.affiliation: School of Computer Science and Technology, Civil Aviation University of China, Tianjin, China; Information Technology Research Base of Civil Aviation Administration of China, Tianjin, China.
authors.forename: Xia
authors.initials: X
authors.lastname: Feng
authors.affiliation: School of Computer Science and Technology, Tianjin University, Tianjin, China; Information Technology Research Base of Civil Aviation Administration of China, Tianjin, China.
authors.forename: Guoqing
authors.initials: G
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019058:Community Networks; D003198:Computer Simulation; D006801:Humans; D008962:Models, Theoretical