A Markov random walk under constraint for discovering overlapping communities in complex networks
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/33790 |
Resumo: | Detection of overlapping communities in complex networks has motivated recent research in the relevant fields. Aiming this problem, we propose a Markov dynamics based algorithm, called UEOC, which means, 'unfold and extract overlapping communities'. In UEOC, when identifying each natural community that overlaps, a Markov random walk method combined with a constraint strategy, which is based on the corresponding annealed network (degree conserving random network), is performed to unfold the community. Then, a cutoff criterion with the aid of a local community function, called conductance, which can be thought of as the ratio between the number of edges inside the community and those leaving it, is presented to extract this emerged community from the entire network. The UEOC algorithm depends on only one parameter whose value can be easily set, and it requires no prior knowledge on the hidden community structures. The proposed UEOC has been evaluated both on synthetic benchmarks and on some real-world networks, and was compared with a set of competing algorithms. Experimental result has shown that UEOC is highly effective and efficient for discovering overlapping communities. |
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A Markov random walk under constraint for discovering overlapping communities in complex networksAnalysis of algorithmsClustering techniquesNetwork dynamicsrandom graphsnetworksScience & TechnologyDetection of overlapping communities in complex networks has motivated recent research in the relevant fields. Aiming this problem, we propose a Markov dynamics based algorithm, called UEOC, which means, 'unfold and extract overlapping communities'. In UEOC, when identifying each natural community that overlaps, a Markov random walk method combined with a constraint strategy, which is based on the corresponding annealed network (degree conserving random network), is performed to unfold the community. Then, a cutoff criterion with the aid of a local community function, called conductance, which can be thought of as the ratio between the number of edges inside the community and those leaving it, is presented to extract this emerged community from the entire network. The UEOC algorithm depends on only one parameter whose value can be easily set, and it requires no prior knowledge on the hidden community structures. The proposed UEOC has been evaluated both on synthetic benchmarks and on some real-world networks, and was compared with a set of competing algorithms. Experimental result has shown that UEOC is highly effective and efficient for discovering overlapping communities.This work was supported by the National Natural Science Foundation of China under Grant Nos 60873149, 60973088, the National High-Tech Research and Development Plan of China under Grant No. 2006AA10Z245, the Open Project Program of the National Laboratory of Pattern Recognition, and the Erasmus Mundus Project of the European Commission.IOP PublishingIOP PublishingUniversidade do MinhoBaquero, CarlosDayou LiuBo YangDi JinJie LiuDongxiao He20112011-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/33790eng1742-546810.1088/1742-5468/2011/05/P05031info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:27:46Zoai:repositorium.sdum.uminho.pt:1822/33790Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:22:29.234226Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
A Markov random walk under constraint for discovering overlapping communities in complex networks |
title |
A Markov random walk under constraint for discovering overlapping communities in complex networks |
spellingShingle |
A Markov random walk under constraint for discovering overlapping communities in complex networks Baquero, Carlos Analysis of algorithms Clustering techniques Network dynamics random graphs networks Science & Technology |
title_short |
A Markov random walk under constraint for discovering overlapping communities in complex networks |
title_full |
A Markov random walk under constraint for discovering overlapping communities in complex networks |
title_fullStr |
A Markov random walk under constraint for discovering overlapping communities in complex networks |
title_full_unstemmed |
A Markov random walk under constraint for discovering overlapping communities in complex networks |
title_sort |
A Markov random walk under constraint for discovering overlapping communities in complex networks |
author |
Baquero, Carlos |
author_facet |
Baquero, Carlos Dayou Liu Bo Yang Di Jin Jie Liu Dongxiao He |
author_role |
author |
author2 |
Dayou Liu Bo Yang Di Jin Jie Liu Dongxiao He |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Baquero, Carlos Dayou Liu Bo Yang Di Jin Jie Liu Dongxiao He |
dc.subject.por.fl_str_mv |
Analysis of algorithms Clustering techniques Network dynamics random graphs networks Science & Technology |
topic |
Analysis of algorithms Clustering techniques Network dynamics random graphs networks Science & Technology |
description |
Detection of overlapping communities in complex networks has motivated recent research in the relevant fields. Aiming this problem, we propose a Markov dynamics based algorithm, called UEOC, which means, 'unfold and extract overlapping communities'. In UEOC, when identifying each natural community that overlaps, a Markov random walk method combined with a constraint strategy, which is based on the corresponding annealed network (degree conserving random network), is performed to unfold the community. Then, a cutoff criterion with the aid of a local community function, called conductance, which can be thought of as the ratio between the number of edges inside the community and those leaving it, is presented to extract this emerged community from the entire network. The UEOC algorithm depends on only one parameter whose value can be easily set, and it requires no prior knowledge on the hidden community structures. The proposed UEOC has been evaluated both on synthetic benchmarks and on some real-world networks, and was compared with a set of competing algorithms. Experimental result has shown that UEOC is highly effective and efficient for discovering overlapping communities. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011 2011-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/33790 |
url |
http://hdl.handle.net/1822/33790 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1742-5468 10.1088/1742-5468/2011/05/P05031 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IOP Publishing IOP Publishing |
publisher.none.fl_str_mv |
IOP Publishing IOP Publishing |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132695892066304 |