Speaker: Yan Ting, Professor, School of Mathematics and Statistics, Central China Normal University

Date: Nov. 17, 2021

Time: 10:00-11:00

Location: Tencent Meeting, ID:918 888 408

Sponsor: School of Mathematics, Shandong University

Abstract:

The beta model is a powerful tool for modeling network generation driven by node degree heterogeneity. It is simple yet expressive nature particularly well-suits large and sparse networks, where even moderately complex models might be infeasible to fit due to very few nonzero observations and computational challenges. However, simple as this model is, our theoretical understanding remains rather limited. Also, available computation method for fitting this model remains unscalable. In a big-data era, substantial improvements are urgently needed for the beta model. Our paper brings several major refinements and improvements to the methodology and theory of the beta model: 1. we propose a new L2 penalized MLE scheme; we design a novel algorithm that can comfortably handle sparse networks of millions of nodes, sharply contrasting the best existing tools that could only deal with thousands of nodes; 2. we present much stronger error bounds on beta-models under much weaker assumptions than existing literature; we also prove the first resolution-limit bound and new normality results; 3. we apply our method to analyze a huge COVID-19 knowledge graph and discover very meaningful results.

For more information, please visit:

https://www.view.sdu.edu.cn/info/1020/159052.htm