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

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 cri...

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Detalles Bibliográficos
Autores principales: Kawamoto, Tatsuro, Kabashima, Yoshiyuki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5468368/
https://www.ncbi.nlm.nih.gov/pubmed/28607441
http://dx.doi.org/10.1038/s41598-017-03623-x
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author Kawamoto, Tatsuro
Kabashima, Yoshiyuki
author_facet Kawamoto, Tatsuro
Kabashima, Yoshiyuki
author_sort Kawamoto, Tatsuro
collection PubMed
description 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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spelling pubmed-54683682017-06-14 Cross-validation estimate of the number of clusters in a network Kawamoto, Tatsuro Kabashima, Yoshiyuki Sci Rep Article 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. Nature Publishing Group UK 2017-06-12 /pmc/articles/PMC5468368/ /pubmed/28607441 http://dx.doi.org/10.1038/s41598-017-03623-x Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Kawamoto, Tatsuro
Kabashima, Yoshiyuki
Cross-validation estimate of the number of clusters in a network
title 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_short Cross-validation estimate of the number of clusters in a network
title_sort cross-validation estimate of the number of clusters in a network
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5468368/
https://www.ncbi.nlm.nih.gov/pubmed/28607441
http://dx.doi.org/10.1038/s41598-017-03623-x
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