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【95周年校庆系列讲座】Popularity-Adjusted Block Models for Networks with Community Structure

时间: 2019-07-17         阅读:

光华讲坛——社会名流与企业家论坛第 5759 期

(线上讲座)

主题: Popularity-Adjusted Block Models for Networks with Community Structure

主讲人: 外围买彩票app伊利诺伊大学香槟分校 陈玉国教授

主持人: 统计学院 林华珍教授

时间: 2020年7月20日(周一)11:00-12:00

直播平台及会议ID: 外围买彩票appZOOM,会议ID: 421 568 770

主办单位:外围买彩票app_靠谱的外围买球app_【官网】 统计研究中心、数据科学与商业智能联合实验室、统计学院、科研处

主讲人简介:

陈玉国,伊利诺伊大学香槟分校统计系教授,Data Science Founder Professorial Scholar,统计咨询中心主任。2001年在斯坦福大学取得统计学博士学位,他是美国统计协会(ASA)的Fellow.曾担任Journal of American Statistical Association, Journal of Computational and Graphical Statistics,和Algebraic Statistics副主编。他的研究兴趣包括网络数据分析,蒙特卡洛方法,和动态系统。详情请见其个人主页: http://publish.illinois.edu/yuguo/


内容提要:

The community structure observed in empirical networks has been of particular interest in the statistics literature, with a strong emphasis on the study of block models. We study an important network feature called node popularity, which is closely associated with community structure. Neither the classical stochastic block model nor its degree-corrected extension can satisfactorily capture the dynamics of node popularity as observed in empirical networks. We propose a popularity-adjusted block model for flexible and realistic modeling of node popularity. We establish consistency of likelihood modularity for community detection as well as estimation of node popularities and model parameters, and demonstrate the advantages of the new modularity over the degree-corrected block model modularity in simulations. By analyzing the political blogs network, the British MP network, and the DBLP bibliographical network, we illustrate that improved empirical insights can be gained through this methodology.

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