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 asses...

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Autores principales:	Tatsuro Kawamoto, Yoshiyuki Kabashima
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e3dda50ef2b04a89ac1abdae0005a189
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spelling	oai:doaj.org-article:e3dda50ef2b04a89ac1abdae0005a1892021-12-02T11:52:25ZCross-validation estimate of the number of clusters in a network10.1038/s41598-017-03623-x2045-2322https://doaj.org/article/e3dda50ef2b04a89ac1abdae0005a1892017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-03623-xhttps://doaj.org/toc/2045-2322Abstract 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.Tatsuro KawamotoYoshiyuki KabashimaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-17 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tatsuro Kawamoto Yoshiyuki Kabashima Cross-validation estimate of the number of clusters in a network
description	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.
format	article
author	Tatsuro Kawamoto Yoshiyuki Kabashima
author_facet	Tatsuro Kawamoto Yoshiyuki Kabashima
author_sort	Tatsuro Kawamoto
title	Cross-validation estimate of the number of clusters in a network
title_short	Cross-validation estimate of the number of clusters in a network
title_full	Cross-validation estimate of the number of clusters in a network
title_fullStr	Cross-validation estimate of the number of clusters in a network
title_full_unstemmed	Cross-validation estimate of the number of clusters in a network
title_sort	cross-validation estimate of the number of clusters in a network
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/e3dda50ef2b04a89ac1abdae0005a189
work_keys_str_mv	AT tatsurokawamoto crossvalidationestimateofthenumberofclustersinanetwork AT yoshiyukikabashima crossvalidationestimateofthenumberofclustersinanetwork
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Cross-validation estimate of the number of clusters in a network

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