Journal
SCIENTIFIC REPORTS
Volume 7, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41598-017-03623-x
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Funding
- JSPS KAKENHI [26011023, 25120013]
- Grants-in-Aid for Scientific Research [17H00764] Funding Source: KAKEN
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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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