Obtaining communities with a fitness growth process

Autores
Beiro, Mariano Gastón; Busch, Jorge R.; Grynberg, Sebastian P.; Alvarez Hamelin, Jose Ignacio
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The study of community structure became an important topic of research over the last years. But, while successfully applied in several areas, the concept lacks of a general and precise notion. Facts like the hierarchical structure and heterogeneity of complex networks make it difficult to unify the idea of community and its evaluation. The global functional known as modularity is probably the most used technique in this area. Nevertheless, its limits have been deeply studied. Local techniques as the one by Lancichinetti et al. (2009) [1] arose as an answer to the resolution limit and degeneracies that modularity has. Here we propose a unique growth process for a fitness function based on the algorithm by Lancichinetti et al. (2009) [1]. The process is local and finds a community partition that covers the whole network, updating the scale parameter dynamically. We test the quality of our results by using a set of benchmarks of both heterogeneous and homogeneous graphs. We discuss alternative measures for evaluating the community structure and, in the light of them, infer possible explanations for the better performance of local methods compared to global ones in these cases.
Fil: Beiro, Mariano Gastón. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Electronica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería; Argentina
Fil: Busch, Jorge R.. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Matematicas; Argentina
Fil: Grynberg, Sebastian P.. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Matematicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería; Argentina
Fil: Alvarez Hamelin, Jose Ignacio. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Electronica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería; Argentina
Materia
Community Detection
Social Networks
Complex Systems
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/12533

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spelling Obtaining communities with a fitness growth processBeiro, Mariano GastónBusch, Jorge R.Grynberg, Sebastian P.Alvarez Hamelin, Jose IgnacioCommunity DetectionSocial NetworksComplex Systemshttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1The study of community structure became an important topic of research over the last years. But, while successfully applied in several areas, the concept lacks of a general and precise notion. Facts like the hierarchical structure and heterogeneity of complex networks make it difficult to unify the idea of community and its evaluation. The global functional known as modularity is probably the most used technique in this area. Nevertheless, its limits have been deeply studied. Local techniques as the one by Lancichinetti et al. (2009) [1] arose as an answer to the resolution limit and degeneracies that modularity has. Here we propose a unique growth process for a fitness function based on the algorithm by Lancichinetti et al. (2009) [1]. The process is local and finds a community partition that covers the whole network, updating the scale parameter dynamically. We test the quality of our results by using a set of benchmarks of both heterogeneous and homogeneous graphs. We discuss alternative measures for evaluating the community structure and, in the light of them, infer possible explanations for the better performance of local methods compared to global ones in these cases.Fil: Beiro, Mariano Gastón. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Electronica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería; ArgentinaFil: Busch, Jorge R.. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Matematicas; ArgentinaFil: Grynberg, Sebastian P.. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Matematicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería; ArgentinaFil: Alvarez Hamelin, Jose Ignacio. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Electronica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería; ArgentinaElsevier Science2013-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/12533Beiro, Mariano Gastón; Busch, Jorge R.; Grynberg, Sebastian P.; Alvarez Hamelin, Jose Ignacio; Obtaining communities with a fitness growth process; Elsevier Science; Physica A: Statistical Mechanics And Its Applications; 392; 9; 5-2013; 2278-22930378-4371enginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0378437113000654info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2013.01.031info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:26:50Zoai:ri.conicet.gov.ar:11336/12533instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 15:26:51.23CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Obtaining communities with a fitness growth process
title Obtaining communities with a fitness growth process
spellingShingle Obtaining communities with a fitness growth process
Beiro, Mariano Gastón
Community Detection
Social Networks
Complex Systems
title_short Obtaining communities with a fitness growth process
title_full Obtaining communities with a fitness growth process
title_fullStr Obtaining communities with a fitness growth process
title_full_unstemmed Obtaining communities with a fitness growth process
title_sort Obtaining communities with a fitness growth process
dc.creator.none.fl_str_mv Beiro, Mariano Gastón
Busch, Jorge R.
Grynberg, Sebastian P.
Alvarez Hamelin, Jose Ignacio
author Beiro, Mariano Gastón
author_facet Beiro, Mariano Gastón
Busch, Jorge R.
Grynberg, Sebastian P.
Alvarez Hamelin, Jose Ignacio
author_role author
author2 Busch, Jorge R.
Grynberg, Sebastian P.
Alvarez Hamelin, Jose Ignacio
author2_role author
author
author
dc.subject.none.fl_str_mv Community Detection
Social Networks
Complex Systems
topic Community Detection
Social Networks
Complex Systems
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The study of community structure became an important topic of research over the last years. But, while successfully applied in several areas, the concept lacks of a general and precise notion. Facts like the hierarchical structure and heterogeneity of complex networks make it difficult to unify the idea of community and its evaluation. The global functional known as modularity is probably the most used technique in this area. Nevertheless, its limits have been deeply studied. Local techniques as the one by Lancichinetti et al. (2009) [1] arose as an answer to the resolution limit and degeneracies that modularity has. Here we propose a unique growth process for a fitness function based on the algorithm by Lancichinetti et al. (2009) [1]. The process is local and finds a community partition that covers the whole network, updating the scale parameter dynamically. We test the quality of our results by using a set of benchmarks of both heterogeneous and homogeneous graphs. We discuss alternative measures for evaluating the community structure and, in the light of them, infer possible explanations for the better performance of local methods compared to global ones in these cases.
Fil: Beiro, Mariano Gastón. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Electronica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería; Argentina
Fil: Busch, Jorge R.. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Matematicas; Argentina
Fil: Grynberg, Sebastian P.. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Matematicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería; Argentina
Fil: Alvarez Hamelin, Jose Ignacio. Universidad de Buenos Aires. Facultad de Ingenieria. Departamento de Electronica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería; Argentina
description The study of community structure became an important topic of research over the last years. But, while successfully applied in several areas, the concept lacks of a general and precise notion. Facts like the hierarchical structure and heterogeneity of complex networks make it difficult to unify the idea of community and its evaluation. The global functional known as modularity is probably the most used technique in this area. Nevertheless, its limits have been deeply studied. Local techniques as the one by Lancichinetti et al. (2009) [1] arose as an answer to the resolution limit and degeneracies that modularity has. Here we propose a unique growth process for a fitness function based on the algorithm by Lancichinetti et al. (2009) [1]. The process is local and finds a community partition that covers the whole network, updating the scale parameter dynamically. We test the quality of our results by using a set of benchmarks of both heterogeneous and homogeneous graphs. We discuss alternative measures for evaluating the community structure and, in the light of them, infer possible explanations for the better performance of local methods compared to global ones in these cases.
publishDate 2013
dc.date.none.fl_str_mv 2013-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/12533
Beiro, Mariano Gastón; Busch, Jorge R.; Grynberg, Sebastian P.; Alvarez Hamelin, Jose Ignacio; Obtaining communities with a fitness growth process; Elsevier Science; Physica A: Statistical Mechanics And Its Applications; 392; 9; 5-2013; 2278-2293
0378-4371
url http://hdl.handle.net/11336/12533
identifier_str_mv Beiro, Mariano Gastón; Busch, Jorge R.; Grynberg, Sebastian P.; Alvarez Hamelin, Jose Ignacio; Obtaining communities with a fitness growth process; Elsevier Science; Physica A: Statistical Mechanics And Its Applications; 392; 9; 5-2013; 2278-2293
0378-4371
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0378437113000654
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2013.01.031
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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