A Sparse Beta Regression Model for Network Analysis

• 成果类型: Article
• 署名作者: Stein, Stefan; Feng, Rui; Leng, Chenlei
• 署名单位: University of Warwick
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2024.2411073
• 发表日期: 2025
• 页码: 1281-1293
• 关键词: GENERALIZED LINEAR-MODELS; community detection; Random graphs; inference; selection

摘要

For statistical analysis of network data, the beta -model has emerged as a useful tool, thanks to its flexibility in incorporating nodewise heterogeneity and theoretical tractability. To generalize the beta -model, this article proposes the Sparse beta -Regression Model (S beta RM) that unites two research themes developed recently in modeling homophily and sparsity. In particular, we employ differential heterogeneity that assigns weights only to important nodes and propose penalized likelihood with an l1 penalty for parameter estimation. While our estimation method is closely related to the LASSO method for logistic regression, we develop a new theory emphasizing the use of our model for dealing with a parameter regime that can handle sparse networks usually seen in practice. More interestingly, the resulting inference on the homophily parameter demands no debiasing normally employed in LASSO type estimation. We provide extensive simulation and data analysis to illustrate the use of the model. As a special case of our model, we extend the Erd & odblac;s-R & eacute;nyi model by including covariates and develop the associated statistical inference for sparse networks, which may be of independent interest. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.