统计与数据科学系系列学术报告之四百四十九期

时    间：2024年9月6日（周五）10:00-11:00

地    点：史带楼304室

主持人：复旦大学 管理学院 统计与数据科学系 朱仲义 教授

报告人：Prof. Jiashun Jin 

              Department of Statistics & Data Science  Carnegie Mellon University

题   目：Some results on network modeling

摘    要： The block-model family has four popular network models: SBM, MMSBM, DCBM, and DCMM. A fundamental problem is, how well each of these models fits with real networks. We propose GoF-MSCORE as a new Goodness-of-Fit (GoF) metric for DCMM (the broadest one among the four), with two main ideas. The first is to use cycle count statistics as a general recipe for GoF. The second is a novel network fitting scheme. GoF-MSCORE is a flexible GoF approach. We adapt it to all four models in the block-model family. We show that for each of the four models, if the assumed model is correct, then the corresponding GoF metric converges to N(0,1) as the network sizes diverge. We also analyze the powers and show that these metrics are optimal in many settings. For 11 frequentlyused real networks, we use the proposed GoF metrics to show that DCMM fits well with almost all of them. We also show that SBM, DCBM, and MMSBM do not fit well with many of these networks, especially when the networks are relatively large.

个人简介：Jiashun Jin is Professor in Statistics & Data Science and Affiliated Professor in Machine Learning at Carnegie Mellon University. His earlier work was on the analysis of Rare/Weak signals in big data, focusing on the development of (Tukey’s) Higher Criticism and practical False Discovery Rate (FDR) controlling methods. His more recent interest is on the analysis of complex network and text data, where he has led a team collecting a large-scale data set on statistical publications called the MADStat. In these areas, Jin has co-authored three Editor’s Invited Discussion papers and three Editor’s Invited Review papers.

Jin is an elected IMS fellow and an elected ASA fellow, and he has delivered the highly selective IMS Medallion Lecture in 2015 and IMS AoAS (Annals of Applied Statistics) Lecture in 2016. He was also a recipient of the NSF CAREER award and the IMS Tweedie Award. He has served as Associate Editor for several statistical journals and he is currently severing IMS as the IMS Treasurer. Beyond his academic career, Jin has also gained valuable experience in industry by doing research at Two-Sigma Investments and Google LLC.

统计与数据科学系

2024-8-27