报告题目：Extracting communities from networks

时间：2015年6月3（星期三）14：00-15：00

地点：学院南路校区，学术会堂604

报告人：Professor JiZhu,Department of Statistics, University of Michigan

摘要：

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 ElizavetaLevina.

报告人简介：

朱冀教授为威斯尼斯人“手拉手”项目特聘教授，美国密歇根大学教授，斯坦福大学统计学博士。美国统计学会等多个学会会员，曾获得美国自然科学基金CAREER奖。在JASA等顶级统计学期刊发表学术论文60余篇。同时担任Journal of the American Statistical Association等多个国际著名统计学期刊的副主编。

[编辑]：孙颖