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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/12533
Ver los metadatos del registro completo
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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/ |
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openAccess |
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https://creativecommons.org/licenses/by-nc-nd/2.5/ar/ |
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application/pdf application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier Science |
publisher.none.fl_str_mv |
Elsevier Science |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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