报告人：张菁菲
报告地点：腾讯会议ID:997-894-020
报告时间：2022年10月27日星期四9:00-10:00
报告摘要：
One of the fundamental problems in network data analysis is community detection that aims to partition nodes into cohesive communities. The stochastic block model, along with its variants, is one of the most studied statistical models for this purpose. Directly fitting the stochastic block model likelihood function on large-scale networks is known to be challenging. In this talk, I will discuss a pseudo likelihood approach that uses a new idea of “label decoupling” that permits an alternating maximization and can efficiently handle up to millions of nodes. The proposed method has provable convergence guarantee and enjoys good statistical properties. I will also briefly discuss my work on testing for the number of communities in a stochastic block model and finally illustrate the usefulness of our methods through an analysis of international trade data.
主讲人简介：
Dr. Emma Jingfei Zhang is an Associate Professor of Management Science at the Miami Herbert Business School of the University of Miami, where she also holds a secondary appointment in the Department of Public Health Sciences at the Miller School of Medicine. Dr. Zhang received her Ph.D. in Statistics from the University of Illinois at Urbana-Champaign. Her research focuses on the developments of statistical methods and theory for high-dimensional networks, graphs, tensors, and point processes, with applications in business, social sciences and medicine.
