Articles producció científica> Medicina i Cirurgia

Inferring propagation paths for sparsely observed perturbations on complex networks

  • Datos identificativos

    Identificador: imarina:3654327
    Autores:
    Massucci, Francesco AlessandroWheeler, JonathanBeltran-Debon, RaulJoven, JorgeSales-Pardo, MartaGuimera, Roger
    Resumen:
    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
  • Otros:

    Autor según el artículo: Massucci, Francesco Alessandro; Wheeler, Jonathan; Beltran-Debon, Raul; Joven, Jorge; Sales-Pardo, Marta; Guimera, Roger
    Departamento: Medicina i Cirurgia Enginyeria Química Bioquímica i Biotecnologia
    Autor/es de la URV: Beltrán Debón, Raúl Alejandro / Guimera Manrique, Roger / Joven Maried, Jorge / MASSUCCI, FRANCESCO ALESSANDRO / Sales Pardo, Marta
    Palabras clave: Perturbed systems Inference Complex networks Belief propagation inference complex networks belief propagation
    Resumen: 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
    Áreas temáticas: Química Multidisciplinary sciences Multidisciplinary Medicine (miscellaneous) Interdisciplinar Geociências General medicine Engenharias iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Biotecnología Biodiversidade Astronomia / física
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 23752548
    Direcció de correo del autor: roger.guimera@urv.cat jorge.joven@urv.cat marta.sales@urv.cat raul.beltran@urv.cat
    Identificador del autor: 0000-0002-3597-4310 0000-0003-2749-4541 0000-0002-8140-6525 0000-0001-9691-1906
    Fecha de alta del registro: 2024-10-19
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://advances.sciencemag.org/content/2/10/e1501638
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Science Advances. 2 (10): e1501638-
    Referencia de l'ítem segons les normes APA: Massucci, Francesco Alessandro; Wheeler, Jonathan; Beltran-Debon, Raul; Joven, Jorge; Sales-Pardo, Marta; Guimera, Roger (2016). Inferring propagation paths for sparsely observed perturbations on complex networks. Science Advances, 2(10), e1501638-. DOI: 10.1126/sciadv.1501638
    DOI del artículo: 10.1126/sciadv.1501638
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2016
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Medicine (Miscellaneous),Multidisciplinary,Multidisciplinary Sciences
    Perturbed systems
    Inference
    Complex networks
    Belief propagation
    inference
    complex networks
    belief propagation
    Química
    Multidisciplinary sciences
    Multidisciplinary
    Medicine (miscellaneous)
    Interdisciplinar
    Geociências
    General medicine
    Engenharias iii
    Ciências biológicas ii
    Ciências biológicas i
    Ciências ambientais
    Ciências agrárias i
    Biotecnología
    Biodiversidade
    Astronomia / física
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