Avoiding catastrophic failure in correlated network of networks

Autores
Reis, Saulo D. S.; Hu, Yanqing; Babino, Andrés; Andrade, José S. Jr.; Canals, Santiago; Sigman, Mariano; Makse, Hernán Alejandro
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Networks in nature do not act in isolation, but instead exchange information and depend on one another to function properly1–3 . Theory has shown that connecting random networks may very easily result in abrupt failures3–6. This finding reveals an intriguing paradox7,8: if natural systems organize in interconnected networks, how can they be so stable? Here we provide a solution to this conundrum, showing that the stability of a system of networks relies on the relation between the internal structure of a network and its pattern of connections to other networks. Specifically, we demonstrate that if interconnections are provided by network hubs, and the connections between networks are moderately convergent, the system of networks is stable and robust to failure. We test this theoretical prediction on two independent experiments of functional brain networks (in task and resting states), which show that brain networks are connected with a topology that maximizes stability according to the theory
Fil: Reis, Saulo D. S.. Universidade Federal Do Ceara; Brasil. City University Of New York. The City College Of New York.; Estados Unidos
Fil: Hu, Yanqing. City University Of New York. The City College Of New York.; Estados Unidos
Fil: Babino, Andrés. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Andrade, José S. Jr.. Universidade Federal Do Ceara; Brasil
Fil: Canals, Santiago. Consejo Superior de Investigaciones Científicas. Instituto de Neurociencia de Alicante; España
Fil: Sigman, Mariano. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Torcuato Di Tella; Argentina
Fil: Makse, Hernán Alejandro. City University Of New York. The City College Of New York.; Estados Unidos. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Universidade Federal Do Ceara; Brasil
Materia
NETWORKS
NEUROSCIENCE
BRAIN NETWORKS
NETWORK OF NETWORKS
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:11336/110564
Acceder
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spelling Avoiding catastrophic failure in correlated network of networksReis, Saulo D. S.Hu, YanqingBabino, AndrésAndrade, José S. Jr.Canals, SantiagoSigman, MarianoMakse, Hernán AlejandroNETWORKSNEUROSCIENCEBRAIN NETWORKSNETWORK OF NETWORKShttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Networks in nature do not act in isolation, but instead exchange information and depend on one another to function properly1–3 . Theory has shown that connecting random networks may very easily result in abrupt failures3–6. This finding reveals an intriguing paradox7,8: if natural systems organize in interconnected networks, how can they be so stable? Here we provide a solution to this conundrum, showing that the stability of a system of networks relies on the relation between the internal structure of a network and its pattern of connections to other networks. Specifically, we demonstrate that if interconnections are provided by network hubs, and the connections between networks are moderately convergent, the system of networks is stable and robust to failure. We test this theoretical prediction on two independent experiments of functional brain networks (in task and resting states), which show that brain networks are connected with a topology that maximizes stability according to the theoryFil: Reis, Saulo D. S.. Universidade Federal Do Ceara; Brasil. City University Of New York. The City College Of New York.; Estados UnidosFil: Hu, Yanqing. City University Of New York. The City College Of New York.; Estados UnidosFil: Babino, Andrés. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Andrade, José S. Jr.. Universidade Federal Do Ceara; BrasilFil: Canals, Santiago. Consejo Superior de Investigaciones Científicas. Instituto de Neurociencia de Alicante; EspañaFil: Sigman, Mariano. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Torcuato Di Tella; ArgentinaFil: Makse, Hernán Alejandro. City University Of New York. The City College Of New York.; Estados Unidos. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Universidade Federal Do Ceara; BrasilNature Publishing Group2014-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/110564Reis, Saulo D. S.; Hu, Yanqing; Babino, Andrés; Andrade, José S. Jr.; Canals, Santiago; et al.; Avoiding catastrophic failure in correlated network of networks; Nature Publishing Group; Nature Physics; 10; 9-2014; 762-7671745-2473CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.nature.com/nphys/journal/v10/n10/full/nphys3081.htmlinfo:eu-repo/semantics/altIdentifier/doi/10.1038/nphys3081info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/1409.5510info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:22:19Zoai:ri.conicet.gov.ar:11336/110564instacron: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 12:22:19.638CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Avoiding catastrophic failure in correlated network of networks
title Avoiding catastrophic failure in correlated network of networks
spellingShingle Avoiding catastrophic failure in correlated network of networks
Reis, Saulo D. S.
NETWORKS
NEUROSCIENCE
BRAIN NETWORKS
NETWORK OF NETWORKS
title_short Avoiding catastrophic failure in correlated network of networks
title_full Avoiding catastrophic failure in correlated network of networks
title_fullStr Avoiding catastrophic failure in correlated network of networks
title_full_unstemmed Avoiding catastrophic failure in correlated network of networks
title_sort Avoiding catastrophic failure in correlated network of networks
dc.creator.none.fl_str_mv Reis, Saulo D. S.
Hu, Yanqing
Babino, Andrés
Andrade, José S. Jr.
Canals, Santiago
Sigman, Mariano
Makse, Hernán Alejandro
author Reis, Saulo D. S.
author_facet Reis, Saulo D. S.
Hu, Yanqing
Babino, Andrés
Andrade, José S. Jr.
Canals, Santiago
Sigman, Mariano
Makse, Hernán Alejandro
author_role author
author2 Hu, Yanqing
Babino, Andrés
Andrade, José S. Jr.
Canals, Santiago
Sigman, Mariano
Makse, Hernán Alejandro
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv NETWORKS
NEUROSCIENCE
BRAIN NETWORKS
NETWORK OF NETWORKS
topic NETWORKS
NEUROSCIENCE
BRAIN NETWORKS
NETWORK OF NETWORKS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Networks in nature do not act in isolation, but instead exchange information and depend on one another to function properly1–3 . Theory has shown that connecting random networks may very easily result in abrupt failures3–6. This finding reveals an intriguing paradox7,8: if natural systems organize in interconnected networks, how can they be so stable? Here we provide a solution to this conundrum, showing that the stability of a system of networks relies on the relation between the internal structure of a network and its pattern of connections to other networks. Specifically, we demonstrate that if interconnections are provided by network hubs, and the connections between networks are moderately convergent, the system of networks is stable and robust to failure. We test this theoretical prediction on two independent experiments of functional brain networks (in task and resting states), which show that brain networks are connected with a topology that maximizes stability according to the theory
Fil: Reis, Saulo D. S.. Universidade Federal Do Ceara; Brasil. City University Of New York. The City College Of New York.; Estados Unidos
Fil: Hu, Yanqing. City University Of New York. The City College Of New York.; Estados Unidos
Fil: Babino, Andrés. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Andrade, José S. Jr.. Universidade Federal Do Ceara; Brasil
Fil: Canals, Santiago. Consejo Superior de Investigaciones Científicas. Instituto de Neurociencia de Alicante; España
Fil: Sigman, Mariano. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Torcuato Di Tella; Argentina
Fil: Makse, Hernán Alejandro. City University Of New York. The City College Of New York.; Estados Unidos. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Universidade Federal Do Ceara; Brasil
description Networks in nature do not act in isolation, but instead exchange information and depend on one another to function properly1–3 . Theory has shown that connecting random networks may very easily result in abrupt failures3–6. This finding reveals an intriguing paradox7,8: if natural systems organize in interconnected networks, how can they be so stable? Here we provide a solution to this conundrum, showing that the stability of a system of networks relies on the relation between the internal structure of a network and its pattern of connections to other networks. Specifically, we demonstrate that if interconnections are provided by network hubs, and the connections between networks are moderately convergent, the system of networks is stable and robust to failure. We test this theoretical prediction on two independent experiments of functional brain networks (in task and resting states), which show that brain networks are connected with a topology that maximizes stability according to the theory
publishDate 2014
dc.date.none.fl_str_mv 2014-09
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/11336/110564
Reis, Saulo D. S.; Hu, Yanqing; Babino, Andrés; Andrade, José S. Jr.; Canals, Santiago; et al.; Avoiding catastrophic failure in correlated network of networks; Nature Publishing Group; Nature Physics; 10; 9-2014; 762-767
1745-2473
CONICET Digital
CONICET
url http://hdl.handle.net/11336/110564
identifier_str_mv Reis, Saulo D. S.; Hu, Yanqing; Babino, Andrés; Andrade, José S. Jr.; Canals, Santiago; et al.; Avoiding catastrophic failure in correlated network of networks; Nature Publishing Group; Nature Physics; 10; 9-2014; 762-767
1745-2473
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/nphys/journal/v10/n10/full/nphys3081.html
info:eu-repo/semantics/altIdentifier/doi/10.1038/nphys3081
info:eu-repo/semantics/altIdentifier/url/https://arxiv.org/abs/1409.5510
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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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
application/pdf
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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