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Title: Sparse models for sparse networks

Author introduction: Chenlei Leng is Professor of Statistics in the University of Warwick. He received a bachelor's degree in Mathematics from the University of Science and Technology of China and PhD in Statistics from the University of Wisconsin-Madison. He has held regular and visiting faculty positions in Peking University, the University of Munich and the National University of Singapore. He mainly works on developing methods for analyzing high-dimensional data, network data, and correlated data.冷琛雷英国华威大学教授,博士毕业于美国威斯康星大学麦迪逊分校。国际统计学会ISI和国际数理统计学会IMS的会士。主要研究领域:高维数据、网络数据、相依数据等。

Abstract: Networks are ubiquitous in modern society and science.  Stylized features of a typical network include network sparsity, degree heterogeneity and homophily among many others. This talk introduces a framework with a class of sparse models that utilize parameters to explicitly account for these network features. In particular, the degree heterogeneity is characterized by node-specific parametrization while homophily is captured by the use of covariates. To avoid over-parametrization, one of the key assumptions in our framework is to differentially assign node-specific parameters. We start by discussing the sparse \beta model when no covariates are present, and proceed to discuss a generalized model to include covariates. Interestingly for the former we can use \ell_0 penalization to identify and estimate the heterogeneity parameters, while for the latter we resort to penalized logistic regression with an \ell_1 penalty, thus immediately connecting our methodology to the lasso literature. Along the way, we demonstrate the fallacy of what we call data-selective inference, a common practice in the literature to discard less well-connected nodes in order to fit a model, which can be of independent interest.

邀请人:赵俊龙

报告时间:20211011(周一),下午6:00-7:00

报告方式:腾讯会议——会议号241422663:,密码123456