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Cross-validation estimate of the number of clusters in a network
May 25, 2016 Β· Declared Dead Β· π Scientific Reports
"No code URL or promise found in abstract"
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Authors
Tatsuro Kawamoto, Yoshiyuki Kabashima
arXiv ID
1605.07915
Category
cs.SI: Social & Info Networks
Cross-listed
physics.soc-ph
Citations
32
Venue
Scientific Reports
Last Checked
6 months ago
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.
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