报告人： 王景丽 （南开大学）
报告时间： 6月8日 20:00-21:00
腾讯会议：533-940-991
报告摘要：Mixed membership community detection is a challenging problem in network analysis. Previous spectral clustering algorithms for this problem are developed based on the adjacency matrix instead of regularized Laplacian matrix. To close this gap, under the popular mixed membership stochastic block models (MMSB), this article proposes two efficient spectral clustering algorithms based on an application of the regularized Laplacian matrix, the Simplex Regularized Spectral Clustering (SRSC) algorithm, and the Cone Regularized Spectral Clustering (CRSC) algorithm. SRSC and CRSC are developed based on the simplex structure and the cone structure in the variants of the eigen-decomposition of the regularized Laplacian matrix. We show that these two approaches SRSC and CRSC are asymptotically consistent under mild conditions by providing error bounds for the estimated membership vector of each node under MMSB. These two proposed approaches are successfully applied to synthetic and empirical networks with encouraging results compared with some benchmark methods.
报告人介绍：王景丽，南开大学统计与数据科学学院讲师。2019年博士毕业于新加坡国立大学。主要从事生物统计及网络结构数据的研究。在JBES，SIM，KBS等国际学术期刊上发表论文数篇，主持国家自然科学基金青年项目1项。
邀请人：马学俊