Network cross-validation by edge sampling

• 成果类型: Article
• 署名作者: Li, Tianxi; Levina, Elizaveta; Zhu, Ji
• 署名单位: University of Virginia; University of Michigan System; University of Michigan
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asaa006
• 发表日期: 2020
• 页码: 257276
• 关键词: Community Detection; model selection; bayesian-inference; citation networks; block model; matrix; regularization; Consistency; likelihood; coauthorship

摘要

While many statistical models and methods are now available for network analysis, resampling of network data remains a challenging problem. Cross-validation is a useful general tool for model selection and parameter tuning, but it is not directly applicable to networks since splitting network nodes into groups requires deleting edges and destroys some of the network structure. In this paper we propose a new network resampling strategy, based on splitting node pairs rather than nodes, that is applicable to cross-validation for a wide range of network model selection tasks. We provide theoretical justification for our method in a general setting and examples of how the method can be used in specific network model selection and parameter tuning tasks. Numerical results on simulated networks and on a statisticians' citation network show that the proposed cross-validation approach works well for model selection.