2025/06/17 by Yu Chen, Yang, Bokai, Chen, Yuanxing +1
Computer Science · Physics and Astronomy · Psychology · #05C80(Secondary) #62G20(Primary) 62H30 #Advanced Graph Neural Networks #Complex Network Analysis Techniques #FOS: Computer and information sciences #Mental Health Research Topics #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2506.14244
openalex publication_date 2025/06/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In the network literature, a wide range of statistical models has been proposed to exploit structural patterns in the data. Therefore, model selection between different models is a fundamental problem. However, there remains a lack of systematic theoretical understanding for this problem when comparing across different model classes. In this paper, to address this challenging problem, we propose a penalized edge-sampling cross-validation framework for nested network model selection. By incorporating a model complexity penalty into the evaluation process, our method effectively mitigates the overfitting tendency of cross-validation and adapts to varying model structures. This framework supports comparisons among widely used models, including stochastic block models (SBMs), degree-corrected SBMs (DCBMs), and graphon models, providing the first consistency guarantees for model selection across these settings to our knowledge. Empirical evaluations, including both simulated data and the ``Political Books'' network, demonstrate that our method yields stable and accurate performance across various scenarios.