Google matrix analysis of directed networks

Autores
Ermann, Leonardo; Frahm, Klaus M.; Shepelyansky, Dima L.
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In the past decade modern societies have developed enormous communication and social networks. Their classification and information retrieval processing has become a formidable task for the society. Because of the rapid growth of the World Wide Web, and social and communication networks, new mathematical methods have been invented to characterize the properties of these networks in a more detailed and precise way. Various search engines extensively use such methods. It is highly important to develop new tools to classify and rank a massive amount of network information in a way that is adapted to internal network structures and characteristics. This review describes the Google matrix analysis of directed complex networks demonstrating its efficiency using various examples including the World Wide Web, Wikipedia, software architectures, world trade, social and citation networks, brain neural networks, DNA sequences, and Ulam networks. The analytical and numerical matrix methods used in this analysis originate from the fields of Markov chains, quantum chaos, and random matrix theory.
Fil: Ermann, Leonardo. Comisión Nacional de Energía Atómica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Frahm, Klaus M.. Centre National de la Recherche Scientifique; Francia
Fil: Shepelyansky, Dima L.. Centre National de la Recherche Scientifique; Francia
Materia
Complex Networks
Complex Systems
Spectral Properties
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:11336/42056
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/42056
network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Google matrix analysis of directed networksErmann, LeonardoFrahm, Klaus M.Shepelyansky, Dima L.Complex NetworksComplex SystemsSpectral Propertieshttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1In the past decade modern societies have developed enormous communication and social networks. Their classification and information retrieval processing has become a formidable task for the society. Because of the rapid growth of the World Wide Web, and social and communication networks, new mathematical methods have been invented to characterize the properties of these networks in a more detailed and precise way. Various search engines extensively use such methods. It is highly important to develop new tools to classify and rank a massive amount of network information in a way that is adapted to internal network structures and characteristics. This review describes the Google matrix analysis of directed complex networks demonstrating its efficiency using various examples including the World Wide Web, Wikipedia, software architectures, world trade, social and citation networks, brain neural networks, DNA sequences, and Ulam networks. The analytical and numerical matrix methods used in this analysis originate from the fields of Markov chains, quantum chaos, and random matrix theory.Fil: Ermann, Leonardo. Comisión Nacional de Energía Atómica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Frahm, Klaus M.. Centre National de la Recherche Scientifique; FranciaFil: Shepelyansky, Dima L.. Centre National de la Recherche Scientifique; FranciaAmerican Physical Society2015-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/42056Ermann, Leonardo; Frahm, Klaus M.; Shepelyansky, Dima L.; Google matrix analysis of directed networks; American Physical Society; Reviews Of Modern Physics; 87; 4; 10-2015; 1261-13100034-6861CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1103/RevModPhys.87.1261info:eu-repo/semantics/altIdentifier/url/https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.87.1261info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:46:20Zoai:ri.conicet.gov.ar:11336/42056instacron: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:34982025-09-03 09:46:21.037CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Google matrix analysis of directed networks
title Google matrix analysis of directed networks
spellingShingle Google matrix analysis of directed networks
Ermann, Leonardo
Complex Networks
Complex Systems
Spectral Properties
title_short Google matrix analysis of directed networks
title_full Google matrix analysis of directed networks
title_fullStr Google matrix analysis of directed networks
title_full_unstemmed Google matrix analysis of directed networks
title_sort Google matrix analysis of directed networks
dc.creator.none.fl_str_mv Ermann, Leonardo
Frahm, Klaus M.
Shepelyansky, Dima L.
author Ermann, Leonardo
author_facet Ermann, Leonardo
Frahm, Klaus M.
Shepelyansky, Dima L.
author_role author
author2 Frahm, Klaus M.
Shepelyansky, Dima L.
author2_role author
author
dc.subject.none.fl_str_mv Complex Networks
Complex Systems
Spectral Properties
topic Complex Networks
Complex Systems
Spectral Properties
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In the past decade modern societies have developed enormous communication and social networks. Their classification and information retrieval processing has become a formidable task for the society. Because of the rapid growth of the World Wide Web, and social and communication networks, new mathematical methods have been invented to characterize the properties of these networks in a more detailed and precise way. Various search engines extensively use such methods. It is highly important to develop new tools to classify and rank a massive amount of network information in a way that is adapted to internal network structures and characteristics. This review describes the Google matrix analysis of directed complex networks demonstrating its efficiency using various examples including the World Wide Web, Wikipedia, software architectures, world trade, social and citation networks, brain neural networks, DNA sequences, and Ulam networks. The analytical and numerical matrix methods used in this analysis originate from the fields of Markov chains, quantum chaos, and random matrix theory.
Fil: Ermann, Leonardo. Comisión Nacional de Energía Atómica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Frahm, Klaus M.. Centre National de la Recherche Scientifique; Francia
Fil: Shepelyansky, Dima L.. Centre National de la Recherche Scientifique; Francia
description In the past decade modern societies have developed enormous communication and social networks. Their classification and information retrieval processing has become a formidable task for the society. Because of the rapid growth of the World Wide Web, and social and communication networks, new mathematical methods have been invented to characterize the properties of these networks in a more detailed and precise way. Various search engines extensively use such methods. It is highly important to develop new tools to classify and rank a massive amount of network information in a way that is adapted to internal network structures and characteristics. This review describes the Google matrix analysis of directed complex networks demonstrating its efficiency using various examples including the World Wide Web, Wikipedia, software architectures, world trade, social and citation networks, brain neural networks, DNA sequences, and Ulam networks. The analytical and numerical matrix methods used in this analysis originate from the fields of Markov chains, quantum chaos, and random matrix theory.
publishDate 2015
dc.date.none.fl_str_mv 2015-10
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/11336/42056
Ermann, Leonardo; Frahm, Klaus M.; Shepelyansky, Dima L.; Google matrix analysis of directed networks; American Physical Society; Reviews Of Modern Physics; 87; 4; 10-2015; 1261-1310
0034-6861
CONICET Digital
CONICET
url http://hdl.handle.net/11336/42056
identifier_str_mv Ermann, Leonardo; Frahm, Klaus M.; Shepelyansky, Dima L.; Google matrix analysis of directed networks; American Physical Society; Reviews Of Modern Physics; 87; 4; 10-2015; 1261-1310
0034-6861
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1103/RevModPhys.87.1261
info:eu-repo/semantics/altIdentifier/url/https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.87.1261
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 13.13397

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