On Equivalence of Likelihood Maximization of Stochastic Block Model and Nonnegative Matrix Factorization, and Beyond
主 题: On Equivalence of Likelihood Maximization of Stochastic Block Model and Nonnegative Matrix Factorization, and Beyond
报告人: 张忠元 (中央财经大学)
时 间: 2017-04-20 14:00-15:00
地 点: 理科1号楼1114
Abstract: Community structures detection in complex network is important for understanding not only the topological structures of the network, but also the functions of it. Stochastic block model and nonnegative matrix factorization are two widely used methods for community detection, which are proposed from different perspectives. The relations between them are studied in this talk. The logarithm of likelihood function for stochastic block model can be reformulated under the framework of nonnegative matrix factorization. Besides the model equivalence, the algorithms employed by the two methods are different. Furthermore, we design new matrix factorization model for signed network, and its effectiveness is evaluated.
About the speaker: Zhong-Yuan Zhang is Full Professor at the School of Statistics and Mathematics, Central University of Finance and Economics. He obtained his Ph.D. from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences. His research interests are within the areas of data mining and complex social network analysis. He has received several prestigious awards and have published papers in leading journals such as Physical Review E and Physica A.