Hierarchical benchmark graphs for testing community detection algorithms

Autores
Yang, Zhao; Perotti, Juan Ignacio; Tessone, Claudio J.
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Hierarchical organization is an important, prevalent characteristic of complex systems; to understand their organization, the study of the underlying (generally complex) networks that describe the interactions between their constituents plays a central role. Numerous previous works have shown that many real-world networks in social, biologic, and technical systems present hierarchical organization, often in the form of a hierarchy of community structures. Many artificial benchmark graphs have been proposed to test different community detection methods, but no benchmark has been developed to thoroughly test the detection of hierarchical community structures. In this study, we fill this vacancy by extending the Lancichinetti-Fortunato-Radicchi (LFR) ensemble of benchmark graphs, adopting the rule of constructing hierarchical networks proposed by Ravasz and Barabási. We employ this benchmark to test three of the most popular community detection algorithms and quantify their accuracy using the traditional mutual information and the recently introduced hierarchical mutual information. The results indicate that the Ravasz-Barabási-Lancichinetti-Fortunato-Radicchi (RB-LFR) benchmark generates a complex hierarchical structure constituting a challenging benchmark for the considered community detection methods.
Fil: Yang, Zhao. Universitat Zurich; Suiza
Fil: Perotti, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Imt Institute For Advanced Studies Lucca; Italia
Fil: Tessone, Claudio J.. Imt Institute For Advanced Studies Lucca; Italia. Universitat Zurich; Suiza
Materia
Redes Complejas
Jerarquías
Detección de Comunidades
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/64765
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/64765
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Hierarchical benchmark graphs for testing community detection algorithmsYang, ZhaoPerotti, Juan IgnacioTessone, Claudio J.Redes ComplejasJerarquíasDetección de Comunidadeshttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Hierarchical organization is an important, prevalent characteristic of complex systems; to understand their organization, the study of the underlying (generally complex) networks that describe the interactions between their constituents plays a central role. Numerous previous works have shown that many real-world networks in social, biologic, and technical systems present hierarchical organization, often in the form of a hierarchy of community structures. Many artificial benchmark graphs have been proposed to test different community detection methods, but no benchmark has been developed to thoroughly test the detection of hierarchical community structures. In this study, we fill this vacancy by extending the Lancichinetti-Fortunato-Radicchi (LFR) ensemble of benchmark graphs, adopting the rule of constructing hierarchical networks proposed by Ravasz and Barabási. We employ this benchmark to test three of the most popular community detection algorithms and quantify their accuracy using the traditional mutual information and the recently introduced hierarchical mutual information. The results indicate that the Ravasz-Barabási-Lancichinetti-Fortunato-Radicchi (RB-LFR) benchmark generates a complex hierarchical structure constituting a challenging benchmark for the considered community detection methods.Fil: Yang, Zhao. Universitat Zurich; SuizaFil: Perotti, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Imt Institute For Advanced Studies Lucca; ItaliaFil: Tessone, Claudio J.. Imt Institute For Advanced Studies Lucca; Italia. Universitat Zurich; SuizaAmerican Physical Society2017-11-14info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/64765Yang, Zhao; Perotti, Juan Ignacio; Tessone, Claudio J.; Hierarchical benchmark graphs for testing community detection algorithms; American Physical Society; Physical Review E; 96; 5; 14-11-2017; 52311-523112470-00532470-0045CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://journals.aps.org/pre/abstract/10.1103/PhysRevE.96.052311info:eu-repo/semantics/altIdentifier/doi/10.1103/PhysRevE.96.052311info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/1708.06969info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:12:47Zoai:ri.conicet.gov.ar:11336/64765instacron: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:34982025-10-22 12:12:47.904CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Hierarchical benchmark graphs for testing community detection algorithms
title Hierarchical benchmark graphs for testing community detection algorithms
spellingShingle Hierarchical benchmark graphs for testing community detection algorithms
Yang, Zhao
Redes Complejas
Jerarquías
Detección de Comunidades
title_short Hierarchical benchmark graphs for testing community detection algorithms
title_full Hierarchical benchmark graphs for testing community detection algorithms
title_fullStr Hierarchical benchmark graphs for testing community detection algorithms
title_full_unstemmed Hierarchical benchmark graphs for testing community detection algorithms
title_sort Hierarchical benchmark graphs for testing community detection algorithms
dc.creator.none.fl_str_mv Yang, Zhao
Perotti, Juan Ignacio
Tessone, Claudio J.
author Yang, Zhao
author_facet Yang, Zhao
Perotti, Juan Ignacio
Tessone, Claudio J.
author_role author
author2 Perotti, Juan Ignacio
Tessone, Claudio J.
author2_role author
author
dc.subject.none.fl_str_mv Redes Complejas
Jerarquías
Detección de Comunidades
topic Redes Complejas
Jerarquías
Detección de Comunidades
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Hierarchical organization is an important, prevalent characteristic of complex systems; to understand their organization, the study of the underlying (generally complex) networks that describe the interactions between their constituents plays a central role. Numerous previous works have shown that many real-world networks in social, biologic, and technical systems present hierarchical organization, often in the form of a hierarchy of community structures. Many artificial benchmark graphs have been proposed to test different community detection methods, but no benchmark has been developed to thoroughly test the detection of hierarchical community structures. In this study, we fill this vacancy by extending the Lancichinetti-Fortunato-Radicchi (LFR) ensemble of benchmark graphs, adopting the rule of constructing hierarchical networks proposed by Ravasz and Barabási. We employ this benchmark to test three of the most popular community detection algorithms and quantify their accuracy using the traditional mutual information and the recently introduced hierarchical mutual information. The results indicate that the Ravasz-Barabási-Lancichinetti-Fortunato-Radicchi (RB-LFR) benchmark generates a complex hierarchical structure constituting a challenging benchmark for the considered community detection methods.
Fil: Yang, Zhao. Universitat Zurich; Suiza
Fil: Perotti, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Imt Institute For Advanced Studies Lucca; Italia
Fil: Tessone, Claudio J.. Imt Institute For Advanced Studies Lucca; Italia. Universitat Zurich; Suiza
description Hierarchical organization is an important, prevalent characteristic of complex systems; to understand their organization, the study of the underlying (generally complex) networks that describe the interactions between their constituents plays a central role. Numerous previous works have shown that many real-world networks in social, biologic, and technical systems present hierarchical organization, often in the form of a hierarchy of community structures. Many artificial benchmark graphs have been proposed to test different community detection methods, but no benchmark has been developed to thoroughly test the detection of hierarchical community structures. In this study, we fill this vacancy by extending the Lancichinetti-Fortunato-Radicchi (LFR) ensemble of benchmark graphs, adopting the rule of constructing hierarchical networks proposed by Ravasz and Barabási. We employ this benchmark to test three of the most popular community detection algorithms and quantify their accuracy using the traditional mutual information and the recently introduced hierarchical mutual information. The results indicate that the Ravasz-Barabási-Lancichinetti-Fortunato-Radicchi (RB-LFR) benchmark generates a complex hierarchical structure constituting a challenging benchmark for the considered community detection methods.
publishDate 2017
dc.date.none.fl_str_mv 2017-11-14
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/11336/64765
Yang, Zhao; Perotti, Juan Ignacio; Tessone, Claudio J.; Hierarchical benchmark graphs for testing community detection algorithms; American Physical Society; Physical Review E; 96; 5; 14-11-2017; 52311-52311
2470-0053
2470-0045
CONICET Digital
CONICET
url http://hdl.handle.net/11336/64765
identifier_str_mv Yang, Zhao; Perotti, Juan Ignacio; Tessone, Claudio J.; Hierarchical benchmark graphs for testing community detection algorithms; American Physical Society; Physical Review E; 96; 5; 14-11-2017; 52311-52311
2470-0053
2470-0045
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://journals.aps.org/pre/abstract/10.1103/PhysRevE.96.052311
info:eu-repo/semantics/altIdentifier/doi/10.1103/PhysRevE.96.052311
info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/1708.06969
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Physical Society
publisher.none.fl_str_mv American Physical Society
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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score 12.982451

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