HKU HKU Dept of Statistics & Actuarial Science, HKU

Seminar by Professor Chenlei LENG from University of Warwick, UK

DateFriday, July 15, 2022
Time4:00 p.m. – 5:00 p.m.
Venuevia Zoom
TitleSparse models for sparse networks

Network data are frequently collected in modern society and science. Stylized features of a typical network include network sparsity, degree heterogeneity and homophily. This talk introduces a framework with a class of sparse models that utilize parameters to explicitly account for these network features. In particular, degree heterogeneity is handled by node-specific parameters while homophily is captured by the use of covariates. To avoid over-parametrization due to the former, we differentially assign parameters to nodes that are important in certain sense. We start by discussing the sparse β model when no covariates are present, and proceed to discuss a generalized model to include covariates. Interestingly for the former we can use 0 penalization to identify and estimate the heterogeneity parameters, while for the latter we resort to penalized logistic regression with an 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.

About the speaker