Optimal Network-Guided Covariate Selection for High-Dimensional Data Integration

• 成果类型: Article; Early Access
• 署名作者: Shen, Tao; Wang, Wanjie
• 署名单位: National University of Singapore; National University of Singapore
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2026.2685341
• 发表日期: 2026-07-23
• 关键词: Clustering; Network analysis; regression; Sparse and weak signals; Spectral methods; variable selection; community detection; HIGHER CRITICISM; regularization; features; rare

摘要

Modern data often arise with multiple modalities. For example, covariates and a network are observed on the same subjects, and both contain useful information. Effectively integrating these modalities is important and challenging, especially when the response is unavailable. We study the fundamental covariate selection problem for high-dimensional data by leveraging network information. We propose the Network-Guided Covariate Selection (NGCS) algorithm. NGCS exploits the spectral structure of the network to construct a network-guided screening statistic, and employs data-driven Higher Criticism Thresholding for covariate recovery. We establish consistency guarantees for NGCS under general networks. In particular, under two commonly used network models, we relate the projected signal strength to the individual signal strength, and demonstrate that NGCS is optimal for covariate selection. It could achieve the same rate as supervised learning. We further consider a two-study setting for downstream applications, where the network is observed only in Study 1. For clustering and regression, we propose NG-clu and NG-reg algorithms. NG-clu accurately clusters all subjects, while NG-reg improves prediction by using the post-selection covariate matrix. Experiments on synthetic and real datasets demonstrate the robustness and superior performance of our algorithms across various network models, noise distributions, and signal strengths. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.