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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/59470
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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 Técnicas2025-09-29T10:38:27Zoai: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:34982025-09-29 10:38:27.355CONICET 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 |
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CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
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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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1844614407532838912 |
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13.070432 |