Cross-validation estimate of the number of clusters in a network

Abstract

Network science investigates methodologies that summarise relational data to obtain better interpretability. Identifying modular structures is a fundamental task, and assessment of the coarse-grain level is its crucial step. Here, we propose principled, scalable, and widely applicable assessment criteria to determine the number of clusters in modular networks based on the leave-one-out cross-validation estimate of the edge prediction error.

19 pages, 9 figures

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Cited by in corpus (6)

• Simplicial Closure and higher-order link prediction
• Evaluating Overfit and Underfit in Models of Network Community Structure
• Consistencies and inconsistencies between model selection and link prediction in networks
• Comparative analysis on the selection of number of clusters in community detection
• Democratic summary of public opinions in free-response surveys
• Single-trajectory map equation