时 间：2012年12月20日星期四下午14：30
地 点：浙江大学紫金港校区图书信息中心B楼CAD&CG国家重点实验室402室
报告题目：Extracting communities from networks
报告人：朱冀 教授
主持人：林海 教授
Abstrac：Analysis of networks and in particular discovering communities within networks has been a focus of recent work in several fields, with applications ranging from citation and friendship networks to food webs and gene regulatory networks. Most of the existing community detection methods focus on partitioning the network into cohesive communities, with the expectation of many links between the members of the same community and few links between different communities. However, many real-world networks contain, in addition to communities, a number of sparsely connected nodes that are best classified as "background". To address this problem, we propose a new criterion for community extraction, which aims to separate tightly linked communities from a sparsely connected background, extracting one community at a time. The new criterion is shown to perform well in simulation studies and on several real networks. We also establish asymptotic consistency of the proposed method under the block model assumption. This is joint work with Yunpeng Zhao and Liza Levina.
Short Bio：Professor Zhu obtained his B.Sc. in Physics from Peking University in 1996 and his Ph.D. in Statistics from Stanford University in 2003. He is currently a Professor in the Department of Statistics at the University of Michigan. Professor Zhu is a well recognized researcher in the areas of statistical machine learning and high-dimensional data analysis. He is also interested in applications in computational biology, engineering, finance, marketing and physics.Professor Zhu received a CAREER award from the National Science of Foundation (USA) in 2008, and was elected as the Chair (2011-2012) of the Statistical Learning and Data Mining Section of the American Statistical Association.
