Network cross-validation by edge sampling
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 04, 2020
- Source ID
- 10.1093/biomet/asaa006
Entities
People
- Elizaveta Levina
- Ji Zhu
- Tianxi Li
Organizations
- National Science Foundation
- Office of Naval Research
- University of Michigan
- University of Virginia