Inferring propagation paths for sparsely observed perturbations on complex networks

Autores
Massucci, Francesco Alessandro; Wheeler, Jonathan; Beltrán Debón, Raúl; Joven, Jorge; Sales Pardo, Marta; Guimerà, Roger
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In a complex system, perturbations propagate by following paths on the network of interactions among the system's units. In contrast to what happens with the spreading of epidemics, observations of general perturbations are often very sparse in time (there is a single observation of the perturbed system) and in "space" (only a few perturbed and unperturbed units are observed). A major challenge in many areas, from biology to the social sciences, is to infer the propagation paths from observations of the effects of perturbation under these sparsity conditions. We address this problem and show that it is possible to go beyond the usual approach of using the shortest paths connecting the known perturbed nodes. Specifically, we show that a simple and general probabilistic model, which we solved using belief propagation, provides fast and accurate estimates of the probabilities of nodes being perturbed.
Fil: Massucci, Francesco Alessandro. Universitat Rovira I Virgili; España
Fil: Wheeler, Jonathan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología; Argentina. Universitat Rovira I Virgili; España
Fil: Beltrán Debón, Raúl. Universitat Rovira I Virgili; España
Fil: Joven, Jorge. Universitat Rovira I Virgili; España
Fil: Sales Pardo, Marta. Universitat Rovira I Virgili; España
Fil: Guimerà, Roger. Institució Catalana de Recerca i Estudis Avancats; España. Universitat Rovira I Virgili; España
Materia
COMPLEX NETWORKS
INFERENCE
BELIEF PROPAGATION
PERTURBED SYSTEMS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/59470
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/59470
network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Inferring propagation paths for sparsely observed perturbations on complex networksMassucci, Francesco AlessandroWheeler, JonathanBeltrán Debón, RaúlJoven, JorgeSales Pardo, MartaGuimerà, RogerCOMPLEX NETWORKSINFERENCEBELIEF PROPAGATIONPERTURBED SYSTEMShttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1In a complex system, perturbations propagate by following paths on the network of interactions among the system's units. In contrast to what happens with the spreading of epidemics, observations of general perturbations are often very sparse in time (there is a single observation of the perturbed system) and in "space" (only a few perturbed and unperturbed units are observed). A major challenge in many areas, from biology to the social sciences, is to infer the propagation paths from observations of the effects of perturbation under these sparsity conditions. We address this problem and show that it is possible to go beyond the usual approach of using the shortest paths connecting the known perturbed nodes. Specifically, we show that a simple and general probabilistic model, which we solved using belief propagation, provides fast and accurate estimates of the probabilities of nodes being perturbed.Fil: Massucci, Francesco Alessandro. Universitat Rovira I Virgili; EspañaFil: Wheeler, Jonathan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología; Argentina. Universitat Rovira I Virgili; EspañaFil: Beltrán Debón, Raúl. Universitat Rovira I Virgili; EspañaFil: Joven, Jorge. Universitat Rovira I Virgili; EspañaFil: Sales Pardo, Marta. Universitat Rovira I Virgili; EspañaFil: Guimerà, Roger. Institució Catalana de Recerca i Estudis Avancats; España. Universitat Rovira I Virgili; EspañaAmerican Association for the Advancement of Science2016-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/59470Massucci, Francesco Alessandro; Wheeler, Jonathan; Beltrán Debón, Raúl; Joven, Jorge; Sales Pardo, Marta; et al.; Inferring propagation paths for sparsely observed perturbations on complex networks; American Association for the Advancement of Science; Science Advances; 2; 10; 10-2016; 1-9; e15016382375-2548CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://advances.sciencemag.org/content/2/10/e1501638.fullinfo:eu-repo/semantics/altIdentifier/doi/10.1126/sciadv.1501638info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T12:10:38Zoai:ri.conicet.gov.ar:11336/59470instacron: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 12:10:38.889CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Inferring propagation paths for sparsely observed perturbations on complex networks
title Inferring propagation paths for sparsely observed perturbations on complex networks
spellingShingle Inferring propagation paths for sparsely observed perturbations on complex networks
Massucci, Francesco Alessandro
COMPLEX NETWORKS
INFERENCE
BELIEF PROPAGATION
PERTURBED SYSTEMS
title_short Inferring propagation paths for sparsely observed perturbations on complex networks
title_full Inferring propagation paths for sparsely observed perturbations on complex networks
title_fullStr Inferring propagation paths for sparsely observed perturbations on complex networks
title_full_unstemmed Inferring propagation paths for sparsely observed perturbations on complex networks
title_sort Inferring propagation paths for sparsely observed perturbations on complex networks
dc.creator.none.fl_str_mv Massucci, Francesco Alessandro
Wheeler, Jonathan
Beltrán Debón, Raúl
Joven, Jorge
Sales Pardo, Marta
Guimerà, Roger
author Massucci, Francesco Alessandro
author_facet Massucci, Francesco Alessandro
Wheeler, Jonathan
Beltrán Debón, Raúl
Joven, Jorge
Sales Pardo, Marta
Guimerà, Roger
author_role author
author2 Wheeler, Jonathan
Beltrán Debón, Raúl
Joven, Jorge
Sales Pardo, Marta
Guimerà, Roger
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv COMPLEX NETWORKS
INFERENCE
BELIEF PROPAGATION
PERTURBED SYSTEMS
topic COMPLEX NETWORKS
INFERENCE
BELIEF PROPAGATION
PERTURBED SYSTEMS
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 a complex system, perturbations propagate by following paths on the network of interactions among the system's units. In contrast to what happens with the spreading of epidemics, observations of general perturbations are often very sparse in time (there is a single observation of the perturbed system) and in "space" (only a few perturbed and unperturbed units are observed). A major challenge in many areas, from biology to the social sciences, is to infer the propagation paths from observations of the effects of perturbation under these sparsity conditions. We address this problem and show that it is possible to go beyond the usual approach of using the shortest paths connecting the known perturbed nodes. Specifically, we show that a simple and general probabilistic model, which we solved using belief propagation, provides fast and accurate estimates of the probabilities of nodes being perturbed.
Fil: Massucci, Francesco Alessandro. Universitat Rovira I Virgili; España
Fil: Wheeler, Jonathan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentina. Universidad Nacional de Tucumán. Facultad de Ciencias Exactas y Tecnología; Argentina. Universitat Rovira I Virgili; España
Fil: Beltrán Debón, Raúl. Universitat Rovira I Virgili; España
Fil: Joven, Jorge. Universitat Rovira I Virgili; España
Fil: Sales Pardo, Marta. Universitat Rovira I Virgili; España
Fil: Guimerà, Roger. Institució Catalana de Recerca i Estudis Avancats; España. Universitat Rovira I Virgili; España
description In a complex system, perturbations propagate by following paths on the network of interactions among the system's units. In contrast to what happens with the spreading of epidemics, observations of general perturbations are often very sparse in time (there is a single observation of the perturbed system) and in "space" (only a few perturbed and unperturbed units are observed). A major challenge in many areas, from biology to the social sciences, is to infer the propagation paths from observations of the effects of perturbation under these sparsity conditions. We address this problem and show that it is possible to go beyond the usual approach of using the shortest paths connecting the known perturbed nodes. Specifically, we show that a simple and general probabilistic model, which we solved using belief propagation, provides fast and accurate estimates of the probabilities of nodes being perturbed.
publishDate 2016
dc.date.none.fl_str_mv 2016-10
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/11336/59470
Massucci, Francesco Alessandro; Wheeler, Jonathan; Beltrán Debón, Raúl; Joven, Jorge; Sales Pardo, Marta; et al.; Inferring propagation paths for sparsely observed perturbations on complex networks; American Association for the Advancement of Science; Science Advances; 2; 10; 10-2016; 1-9; e1501638
2375-2548
CONICET Digital
CONICET
url http://hdl.handle.net/11336/59470
identifier_str_mv Massucci, Francesco Alessandro; Wheeler, Jonathan; Beltrán Debón, Raúl; Joven, Jorge; Sales Pardo, Marta; et al.; Inferring propagation paths for sparsely observed perturbations on complex networks; American Association for the Advancement of Science; Science Advances; 2; 10; 10-2016; 1-9; e1501638
2375-2548
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://advances.sciencemag.org/content/2/10/e1501638.full
info:eu-repo/semantics/altIdentifier/doi/10.1126/sciadv.1501638
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv American Association for the Advancement of Science
publisher.none.fl_str_mv American Association for the Advancement of 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 12.982451

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