时间：2019年7月8日(周一)上午09:30-10:30
地点：中山北路校区理科大楼A1514会议室
题目：Matrix Completion for Network Analysis
报告人：Prof. Ji Zhu （Department of Statistics ，the University of Michigan）
摘要：Matrix completion is an active area of research in itself, and a natural tool to apply to network data, since many real networks are observed incompletely and/or with noise. However, developing matrix completion algorithms for networks requires taking into account the network structure. This talk will discuss three examples of matrix completion used for network tasks. First, we discuss the use of matrix completion for cross-validation or non-parametric bootstrap on network data, a long-standing problem in network analysis. Two other examples focus on reconstructing incompletely observed networks, with structured missingness resulting from network sampling mechanisms. One scenario we consider is egocentric sampling, where a set of nodes is selected first and then their connections to the entire network are observed. Another scenario focuses on data from surveys, where people are asked to name a given number of friends. We show that matrix completion can generally be very helpful in solving network problems, as long as the network structure is taken into account. This talk is based on joint work with Elizaveta Levina, Tianxi Li and Yun-Jhong Wu.
报告人介绍：
Dr. Zhu received his B.Sc. in Physics from Peking University in China, and his Ph.D. in Statistics from Stanford University in 2003. He is now a Professor in the Department of Statistics at the University of Michigan. Dr. Zhu is recognized as a leading researcher in the areas of statistical machine learning and statistical network analysis. He is also interested in applications in health, business, science and engineering. Dr. Zhu received a CAREER award from the National Science Foundation (USA) in 2008, and he was elected as a Fellow of the American Statistical Association in 2013 and a Fellow of the Institute of Mathematical Statistics in 2015; he also served as the Chair-Elect and Chair of the Statistical Learning and Data Science Section of the American Statistical Association from 2011-2013.
