Strategy for stopping failure cascades in interdependent networks

Autores
la Rocca, Cristian Ernesto; Stanley, Harry Eugene; Braunstein, Lidia Adriana
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Interdependencies are ubiquitous throughout the world. Every real-world system interacts with and is dependent on other systems, and this interdependency affects their performance. In particular, interdependencies among networks make them vulnerable to failure cascades, the effects of which are often catastrophic. Failure propagation fragments network components, disconnects them, and may cause complete systemic failure. We propose a strategy of avoiding or at least mitigating the complete destruction of a system of interdependent networks experiencing a failure cascade. Starting with a fraction 1−p of failing nodes in one network, we reconnect with a probability γ every isolated component to a functional giant component (GC), the largest connected cluster. We find that as γ increases the resilience of the system to cascading failure also increases. We also find that our strategy is more effective when it is applied in a network of low average degree. We solve the problem theoretically using percolation theory, and we find that the solution agrees with simulation results.
Fil: la Rocca, Cristian Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina
Fil: Stanley, Harry Eugene. Boston University; Estados Unidos
Fil: Braunstein, Lidia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina
Materia
COMPLEX NETWORKS
INTERDEPENDENT NETWORKS
CASCADE OF FAILURES
PERCOLATION
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:11336/89212
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Strategy for stopping failure cascades in interdependent networksla Rocca, Cristian ErnestoStanley, Harry EugeneBraunstein, Lidia AdrianaCOMPLEX NETWORKSINTERDEPENDENT NETWORKSCASCADE OF FAILURESPERCOLATIONhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1Interdependencies are ubiquitous throughout the world. Every real-world system interacts with and is dependent on other systems, and this interdependency affects their performance. In particular, interdependencies among networks make them vulnerable to failure cascades, the effects of which are often catastrophic. Failure propagation fragments network components, disconnects them, and may cause complete systemic failure. We propose a strategy of avoiding or at least mitigating the complete destruction of a system of interdependent networks experiencing a failure cascade. Starting with a fraction 1−p of failing nodes in one network, we reconnect with a probability γ every isolated component to a functional giant component (GC), the largest connected cluster. We find that as γ increases the resilience of the system to cascading failure also increases. We also find that our strategy is more effective when it is applied in a network of low average degree. We solve the problem theoretically using percolation theory, and we find that the solution agrees with simulation results.Fil: la Rocca, Cristian Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; ArgentinaFil: Stanley, Harry Eugene. Boston University; Estados UnidosFil: Braunstein, Lidia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; ArgentinaElsevier Science2018-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/89212la Rocca, Cristian Ernesto; Stanley, Harry Eugene; Braunstein, Lidia Adriana; Strategy for stopping failure cascades in interdependent networks; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 508; 6-2018; 577-5830378-4371CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2018.05.154info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0378437118307155?via%3Dihubinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:50:29Zoai:ri.conicet.gov.ar:11336/89212instacron: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 09:50:29.578CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Strategy for stopping failure cascades in interdependent networks
title Strategy for stopping failure cascades in interdependent networks
spellingShingle Strategy for stopping failure cascades in interdependent networks
la Rocca, Cristian Ernesto
COMPLEX NETWORKS
INTERDEPENDENT NETWORKS
CASCADE OF FAILURES
PERCOLATION
title_short Strategy for stopping failure cascades in interdependent networks
title_full Strategy for stopping failure cascades in interdependent networks
title_fullStr Strategy for stopping failure cascades in interdependent networks
title_full_unstemmed Strategy for stopping failure cascades in interdependent networks
title_sort Strategy for stopping failure cascades in interdependent networks
dc.creator.none.fl_str_mv la Rocca, Cristian Ernesto
Stanley, Harry Eugene
Braunstein, Lidia Adriana
author la Rocca, Cristian Ernesto
author_facet la Rocca, Cristian Ernesto
Stanley, Harry Eugene
Braunstein, Lidia Adriana
author_role author
author2 Stanley, Harry Eugene
Braunstein, Lidia Adriana
author2_role author
author
dc.subject.none.fl_str_mv COMPLEX NETWORKS
INTERDEPENDENT NETWORKS
CASCADE OF FAILURES
PERCOLATION
topic COMPLEX NETWORKS
INTERDEPENDENT NETWORKS
CASCADE OF FAILURES
PERCOLATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Interdependencies are ubiquitous throughout the world. Every real-world system interacts with and is dependent on other systems, and this interdependency affects their performance. In particular, interdependencies among networks make them vulnerable to failure cascades, the effects of which are often catastrophic. Failure propagation fragments network components, disconnects them, and may cause complete systemic failure. We propose a strategy of avoiding or at least mitigating the complete destruction of a system of interdependent networks experiencing a failure cascade. Starting with a fraction 1−p of failing nodes in one network, we reconnect with a probability γ every isolated component to a functional giant component (GC), the largest connected cluster. We find that as γ increases the resilience of the system to cascading failure also increases. We also find that our strategy is more effective when it is applied in a network of low average degree. We solve the problem theoretically using percolation theory, and we find that the solution agrees with simulation results.
Fil: la Rocca, Cristian Ernesto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina
Fil: Stanley, Harry Eugene. Boston University; Estados Unidos
Fil: Braunstein, Lidia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Físicas de Mar del Plata. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Físicas de Mar del Plata; Argentina
description Interdependencies are ubiquitous throughout the world. Every real-world system interacts with and is dependent on other systems, and this interdependency affects their performance. In particular, interdependencies among networks make them vulnerable to failure cascades, the effects of which are often catastrophic. Failure propagation fragments network components, disconnects them, and may cause complete systemic failure. We propose a strategy of avoiding or at least mitigating the complete destruction of a system of interdependent networks experiencing a failure cascade. Starting with a fraction 1−p of failing nodes in one network, we reconnect with a probability γ every isolated component to a functional giant component (GC), the largest connected cluster. We find that as γ increases the resilience of the system to cascading failure also increases. We also find that our strategy is more effective when it is applied in a network of low average degree. We solve the problem theoretically using percolation theory, and we find that the solution agrees with simulation results.
publishDate 2018
dc.date.none.fl_str_mv 2018-06
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/11336/89212
la Rocca, Cristian Ernesto; Stanley, Harry Eugene; Braunstein, Lidia Adriana; Strategy for stopping failure cascades in interdependent networks; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 508; 6-2018; 577-583
0378-4371
CONICET Digital
CONICET
url http://hdl.handle.net/11336/89212
identifier_str_mv la Rocca, Cristian Ernesto; Stanley, Harry Eugene; Braunstein, Lidia Adriana; Strategy for stopping failure cascades in interdependent networks; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 508; 6-2018; 577-583
0378-4371
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.physa.2018.05.154
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0378437118307155?via%3Dihub
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
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
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 13.070432

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