A detailed characterization of complex networks using Information Theory

Autores
Freitas, Cristopher G. S.; Aquino, Andre L. L.; Ramos, Heitor S.; Frery, Alejandro César; Rosso, Osvaldo Aníbal
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Understanding the structure and the dynamics of networks is of paramount importance for manyscientific fields that rely on network science. Complex network theory provides a variety of features thathelp in the evaluation of network behavior. However, such analysis can be confusing and misleading asthere are many intrinsic properties for each network metric. Alternatively, Information Theory methodshave gained the spotlight because of their ability to create a quantitative and robust characterizationof such networks. In this work, we use two Information Theory quantifiers, namely Network Entropyand Network Fisher Information Measure, to analyzing those networks. Our approach detects nontrivialcharacteristics of complex networks such as the transition present in the Watts-Strogatz modelfrom k-ring to random graphs; the phase transition from a disconnected to an almost surely connectednetwork when we increase the linking probability of Erdős-Rényi model; distinct phases of scale-freenetworks when considering a non-linear preferential attachment, fitness, and aging features alongsidethe configuration model with a pure power-law degree distribution. Finally, we analyze the numericalresults for real networks, contrasting our findings with traditional complex network methods. Inconclusion, we present an efficient method that ignites the debate on network characterization.
Fil: Freitas, Cristopher G. S.. Universidade Federal de Alagoas; Brasil
Fil: Aquino, Andre L. L.. Universidade Federal de Alagoas; Brasil
Fil: Ramos, Heitor S.. Universidade Federal de Minas Gerais; Brasil
Fil: Frery, Alejandro César. Universidade Federal de Alagoas; Brasil
Fil: Rosso, Osvaldo Aníbal. Instituto Universitario del Hospital Italiano de Buenos Aires; Argentina. Universidade Federal de Alagoas; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
COMPLEX NETWORKS
INFORMATION THEORY
SHANNON ENTROPY
FISHER INFORMATION
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:11336/103700
Acceder
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network_name_str CONICET Digital (CONICET)
spelling A detailed characterization of complex networks using Information TheoryFreitas, Cristopher G. S.Aquino, Andre L. L.Ramos, Heitor S.Frery, Alejandro CésarRosso, Osvaldo AníbalCOMPLEX NETWORKSINFORMATION THEORYSHANNON ENTROPYFISHER INFORMATIONhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Understanding the structure and the dynamics of networks is of paramount importance for manyscientific fields that rely on network science. Complex network theory provides a variety of features thathelp in the evaluation of network behavior. However, such analysis can be confusing and misleading asthere are many intrinsic properties for each network metric. Alternatively, Information Theory methodshave gained the spotlight because of their ability to create a quantitative and robust characterizationof such networks. In this work, we use two Information Theory quantifiers, namely Network Entropyand Network Fisher Information Measure, to analyzing those networks. Our approach detects nontrivialcharacteristics of complex networks such as the transition present in the Watts-Strogatz modelfrom k-ring to random graphs; the phase transition from a disconnected to an almost surely connectednetwork when we increase the linking probability of Erdős-Rényi model; distinct phases of scale-freenetworks when considering a non-linear preferential attachment, fitness, and aging features alongsidethe configuration model with a pure power-law degree distribution. Finally, we analyze the numericalresults for real networks, contrasting our findings with traditional complex network methods. Inconclusion, we present an efficient method that ignites the debate on network characterization.Fil: Freitas, Cristopher G. S.. Universidade Federal de Alagoas; BrasilFil: Aquino, Andre L. L.. Universidade Federal de Alagoas; BrasilFil: Ramos, Heitor S.. Universidade Federal de Minas Gerais; BrasilFil: Frery, Alejandro César. Universidade Federal de Alagoas; BrasilFil: Rosso, Osvaldo Aníbal. Instituto Universitario del Hospital Italiano de Buenos Aires; Argentina. Universidade Federal de Alagoas; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaNature Publishing Group2019-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/103700Freitas, Cristopher G. S.; Aquino, Andre L. L.; Ramos, Heitor S.; Frery, Alejandro César; Rosso, Osvaldo Aníbal; A detailed characterization of complex networks using Information Theory; Nature Publishing Group; Scientific Reports; 9; 1; 11-2019; 1-122045-2322CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.nature.com/articles/s41598-019-53167-5info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-019-53167-5info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:49:04Zoai:ri.conicet.gov.ar:11336/103700instacron: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 09:49:04.709CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A detailed characterization of complex networks using Information Theory
title A detailed characterization of complex networks using Information Theory
spellingShingle A detailed characterization of complex networks using Information Theory
Freitas, Cristopher G. S.
COMPLEX NETWORKS
INFORMATION THEORY
SHANNON ENTROPY
FISHER INFORMATION
title_short A detailed characterization of complex networks using Information Theory
title_full A detailed characterization of complex networks using Information Theory
title_fullStr A detailed characterization of complex networks using Information Theory
title_full_unstemmed A detailed characterization of complex networks using Information Theory
title_sort A detailed characterization of complex networks using Information Theory
dc.creator.none.fl_str_mv Freitas, Cristopher G. S.
Aquino, Andre L. L.
Ramos, Heitor S.
Frery, Alejandro César
Rosso, Osvaldo Aníbal
author Freitas, Cristopher G. S.
author_facet Freitas, Cristopher G. S.
Aquino, Andre L. L.
Ramos, Heitor S.
Frery, Alejandro César
Rosso, Osvaldo Aníbal
author_role author
author2 Aquino, Andre L. L.
Ramos, Heitor S.
Frery, Alejandro César
Rosso, Osvaldo Aníbal
author2_role author
author
author
author
dc.subject.none.fl_str_mv COMPLEX NETWORKS
INFORMATION THEORY
SHANNON ENTROPY
FISHER INFORMATION
topic COMPLEX NETWORKS
INFORMATION THEORY
SHANNON ENTROPY
FISHER INFORMATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Understanding the structure and the dynamics of networks is of paramount importance for manyscientific fields that rely on network science. Complex network theory provides a variety of features thathelp in the evaluation of network behavior. However, such analysis can be confusing and misleading asthere are many intrinsic properties for each network metric. Alternatively, Information Theory methodshave gained the spotlight because of their ability to create a quantitative and robust characterizationof such networks. In this work, we use two Information Theory quantifiers, namely Network Entropyand Network Fisher Information Measure, to analyzing those networks. Our approach detects nontrivialcharacteristics of complex networks such as the transition present in the Watts-Strogatz modelfrom k-ring to random graphs; the phase transition from a disconnected to an almost surely connectednetwork when we increase the linking probability of Erdős-Rényi model; distinct phases of scale-freenetworks when considering a non-linear preferential attachment, fitness, and aging features alongsidethe configuration model with a pure power-law degree distribution. Finally, we analyze the numericalresults for real networks, contrasting our findings with traditional complex network methods. Inconclusion, we present an efficient method that ignites the debate on network characterization.
Fil: Freitas, Cristopher G. S.. Universidade Federal de Alagoas; Brasil
Fil: Aquino, Andre L. L.. Universidade Federal de Alagoas; Brasil
Fil: Ramos, Heitor S.. Universidade Federal de Minas Gerais; Brasil
Fil: Frery, Alejandro César. Universidade Federal de Alagoas; Brasil
Fil: Rosso, Osvaldo Aníbal. Instituto Universitario del Hospital Italiano de Buenos Aires; Argentina. Universidade Federal de Alagoas; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Understanding the structure and the dynamics of networks is of paramount importance for manyscientific fields that rely on network science. Complex network theory provides a variety of features thathelp in the evaluation of network behavior. However, such analysis can be confusing and misleading asthere are many intrinsic properties for each network metric. Alternatively, Information Theory methodshave gained the spotlight because of their ability to create a quantitative and robust characterizationof such networks. In this work, we use two Information Theory quantifiers, namely Network Entropyand Network Fisher Information Measure, to analyzing those networks. Our approach detects nontrivialcharacteristics of complex networks such as the transition present in the Watts-Strogatz modelfrom k-ring to random graphs; the phase transition from a disconnected to an almost surely connectednetwork when we increase the linking probability of Erdős-Rényi model; distinct phases of scale-freenetworks when considering a non-linear preferential attachment, fitness, and aging features alongsidethe configuration model with a pure power-law degree distribution. Finally, we analyze the numericalresults for real networks, contrasting our findings with traditional complex network methods. Inconclusion, we present an efficient method that ignites the debate on network characterization.
publishDate 2019
dc.date.none.fl_str_mv 2019-11
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/11336/103700
Freitas, Cristopher G. S.; Aquino, Andre L. L.; Ramos, Heitor S.; Frery, Alejandro César; Rosso, Osvaldo Aníbal; A detailed characterization of complex networks using Information Theory; Nature Publishing Group; Scientific Reports; 9; 1; 11-2019; 1-12
2045-2322
CONICET Digital
CONICET
url http://hdl.handle.net/11336/103700
identifier_str_mv Freitas, Cristopher G. S.; Aquino, Andre L. L.; Ramos, Heitor S.; Frery, Alejandro César; Rosso, Osvaldo Aníbal; A detailed characterization of complex networks using Information Theory; Nature Publishing Group; Scientific Reports; 9; 1; 11-2019; 1-12
2045-2322
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.nature.com/articles/s41598-019-53167-5
info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-019-53167-5
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 Nature Publishing Group
publisher.none.fl_str_mv Nature Publishing Group
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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