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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/110564
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T10:47:50Zoai: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:34982025-09-29 10:47:51.052CONICET 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 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 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 |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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