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Consensus clustering in complex networks

The community structure of complex networks reveals both their organization and hidden relationships among their constituents. Most community detection methods currently available are not deterministic, and their results typically depend on the specific random seeds, initial conditions and tie-break...

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Detalles Bibliográficos
Autores principales: Lancichinetti, Andrea, Fortunato, Santo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3313482/
https://www.ncbi.nlm.nih.gov/pubmed/22468223
http://dx.doi.org/10.1038/srep00336
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author Lancichinetti, Andrea
Fortunato, Santo
author_facet Lancichinetti, Andrea
Fortunato, Santo
author_sort Lancichinetti, Andrea
collection PubMed
description The community structure of complex networks reveals both their organization and hidden relationships among their constituents. Most community detection methods currently available are not deterministic, and their results typically depend on the specific random seeds, initial conditions and tie-break rules adopted for their execution. Consensus clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods. Here we show that consensus clustering can be combined with any existing method in a self-consistent way, enhancing considerably both the stability and the accuracy of the resulting partitions. This framework is also particularly suitable to monitor the evolution of community structure in temporal networks. An application of consensus clustering to a large citation network of physics papers demonstrates its capability to keep track of the birth, death and diversification of topics.
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spelling pubmed-33134822012-03-30 Consensus clustering in complex networks Lancichinetti, Andrea Fortunato, Santo Sci Rep Article The community structure of complex networks reveals both their organization and hidden relationships among their constituents. Most community detection methods currently available are not deterministic, and their results typically depend on the specific random seeds, initial conditions and tie-break rules adopted for their execution. Consensus clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods. Here we show that consensus clustering can be combined with any existing method in a self-consistent way, enhancing considerably both the stability and the accuracy of the resulting partitions. This framework is also particularly suitable to monitor the evolution of community structure in temporal networks. An application of consensus clustering to a large citation network of physics papers demonstrates its capability to keep track of the birth, death and diversification of topics. Nature Publishing Group 2012-03-27 /pmc/articles/PMC3313482/ /pubmed/22468223 http://dx.doi.org/10.1038/srep00336 Text en Copyright © 2012, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-sa/3.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareALike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/
spellingShingle Article
Lancichinetti, Andrea
Fortunato, Santo
Consensus clustering in complex networks
title Consensus clustering in complex networks
title_full Consensus clustering in complex networks
title_fullStr Consensus clustering in complex networks
title_full_unstemmed Consensus clustering in complex networks
title_short Consensus clustering in complex networks
title_sort consensus clustering in complex networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3313482/
https://www.ncbi.nlm.nih.gov/pubmed/22468223
http://dx.doi.org/10.1038/srep00336
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