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Significant Communities in Large Sparse Networks

Researchers use community-detection algorithms to reveal large-scale organization in biological and social networks, but community detection is useful only if the communities are significant and not a result of noisy data. To assess the statistical significance of the network communities, or the rob...

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Autores principales: Mirshahvalad, Atieh, Lindholm, Johan, Derlén, Mattias, Rosvall, Martin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3316493/
https://www.ncbi.nlm.nih.gov/pubmed/22479433
http://dx.doi.org/10.1371/journal.pone.0033721
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author Mirshahvalad, Atieh
Lindholm, Johan
Derlén, Mattias
Rosvall, Martin
author_facet Mirshahvalad, Atieh
Lindholm, Johan
Derlén, Mattias
Rosvall, Martin
author_sort Mirshahvalad, Atieh
collection PubMed
description Researchers use community-detection algorithms to reveal large-scale organization in biological and social networks, but community detection is useful only if the communities are significant and not a result of noisy data. To assess the statistical significance of the network communities, or the robustness of the detected structure, one approach is to perturb the network structure by removing links and measure how much the communities change. However, perturbing sparse networks is challenging because they are inherently sensitive; they shatter easily if links are removed. Here we propose a simple method to perturb sparse networks and assess the significance of their communities. We generate resampled networks by adding extra links based on local information, then we aggregate the information from multiple resampled networks to find a coarse-grained description of significant clusters. In addition to testing our method on benchmark networks, we use our method on the sparse network of the European Court of Justice (ECJ) case law, to detect significant and insignificant areas of law. We use our significance analysis to draw a map of the ECJ case law network that reveals the relations between the areas of law.
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spelling pubmed-33164932012-04-04 Significant Communities in Large Sparse Networks Mirshahvalad, Atieh Lindholm, Johan Derlén, Mattias Rosvall, Martin PLoS One Research Article Researchers use community-detection algorithms to reveal large-scale organization in biological and social networks, but community detection is useful only if the communities are significant and not a result of noisy data. To assess the statistical significance of the network communities, or the robustness of the detected structure, one approach is to perturb the network structure by removing links and measure how much the communities change. However, perturbing sparse networks is challenging because they are inherently sensitive; they shatter easily if links are removed. Here we propose a simple method to perturb sparse networks and assess the significance of their communities. We generate resampled networks by adding extra links based on local information, then we aggregate the information from multiple resampled networks to find a coarse-grained description of significant clusters. In addition to testing our method on benchmark networks, we use our method on the sparse network of the European Court of Justice (ECJ) case law, to detect significant and insignificant areas of law. We use our significance analysis to draw a map of the ECJ case law network that reveals the relations between the areas of law. Public Library of Science 2012-03-30 /pmc/articles/PMC3316493/ /pubmed/22479433 http://dx.doi.org/10.1371/journal.pone.0033721 Text en Mirshahvalad et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Mirshahvalad, Atieh
Lindholm, Johan
Derlén, Mattias
Rosvall, Martin
Significant Communities in Large Sparse Networks
title Significant Communities in Large Sparse Networks
title_full Significant Communities in Large Sparse Networks
title_fullStr Significant Communities in Large Sparse Networks
title_full_unstemmed Significant Communities in Large Sparse Networks
title_short Significant Communities in Large Sparse Networks
title_sort significant communities in large sparse networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3316493/
https://www.ncbi.nlm.nih.gov/pubmed/22479433
http://dx.doi.org/10.1371/journal.pone.0033721
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