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Towards Online Multiresolution Community Detection in Large-Scale Networks

The investigation of community structure in networks has aroused great interest in multiple disciplines. One of the challenges is to find local communities from a starting vertex in a network without global information about the entire network. Many existing methods tend to be accurate depending on...

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Detalles Bibliográficos
Autores principales: Huang, Jianbin, Sun, Heli, Liu, Yaguang, Song, Qinbao, Weninger, Tim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3161084/
https://www.ncbi.nlm.nih.gov/pubmed/21887325
http://dx.doi.org/10.1371/journal.pone.0023829
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author Huang, Jianbin
Sun, Heli
Liu, Yaguang
Song, Qinbao
Weninger, Tim
author_facet Huang, Jianbin
Sun, Heli
Liu, Yaguang
Song, Qinbao
Weninger, Tim
author_sort Huang, Jianbin
collection PubMed
description The investigation of community structure in networks has aroused great interest in multiple disciplines. One of the challenges is to find local communities from a starting vertex in a network without global information about the entire network. Many existing methods tend to be accurate depending on a priori assumptions of network properties and predefined parameters. In this paper, we introduce a new quality function of local community and present a fast local expansion algorithm for uncovering communities in large-scale networks. The proposed algorithm can detect multiresolution community from a source vertex or communities covering the whole network. Experimental results show that the proposed algorithm is efficient and well-behaved in both real-world and synthetic networks.
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spelling pubmed-31610842011-09-01 Towards Online Multiresolution Community Detection in Large-Scale Networks Huang, Jianbin Sun, Heli Liu, Yaguang Song, Qinbao Weninger, Tim PLoS One Research Article The investigation of community structure in networks has aroused great interest in multiple disciplines. One of the challenges is to find local communities from a starting vertex in a network without global information about the entire network. Many existing methods tend to be accurate depending on a priori assumptions of network properties and predefined parameters. In this paper, we introduce a new quality function of local community and present a fast local expansion algorithm for uncovering communities in large-scale networks. The proposed algorithm can detect multiresolution community from a source vertex or communities covering the whole network. Experimental results show that the proposed algorithm is efficient and well-behaved in both real-world and synthetic networks. Public Library of Science 2011-08-24 /pmc/articles/PMC3161084/ /pubmed/21887325 http://dx.doi.org/10.1371/journal.pone.0023829 Text en Huang 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
Huang, Jianbin
Sun, Heli
Liu, Yaguang
Song, Qinbao
Weninger, Tim
Towards Online Multiresolution Community Detection in Large-Scale Networks
title Towards Online Multiresolution Community Detection in Large-Scale Networks
title_full Towards Online Multiresolution Community Detection in Large-Scale Networks
title_fullStr Towards Online Multiresolution Community Detection in Large-Scale Networks
title_full_unstemmed Towards Online Multiresolution Community Detection in Large-Scale Networks
title_short Towards Online Multiresolution Community Detection in Large-Scale Networks
title_sort towards online multiresolution community detection in large-scale networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3161084/
https://www.ncbi.nlm.nih.gov/pubmed/21887325
http://dx.doi.org/10.1371/journal.pone.0023829
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