Articles producció científica> Medicina i Cirurgia

Inferring propagation paths for sparsely observed perturbations on complex networks

  • Identification data

    Identifier: imarina:3654327
    Authors:
    Massucci, Francesco AlessandroWheeler, JonathanBeltran-Debon, RaulJoven, JorgeSales-Pardo, MartaGuimera, Roger
    Abstract:
    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
  • Others:

    Author, as appears in the article.: Massucci, Francesco Alessandro; Wheeler, Jonathan; Beltran-Debon, Raul; Joven, Jorge; Sales-Pardo, Marta; Guimera, Roger
    Department: Medicina i Cirurgia Enginyeria Química Bioquímica i Biotecnologia
    URV's Author/s: Beltrán Debón, Raúl Alejandro / Guimera Manrique, Roger / Joven Maried, Jorge / MASSUCCI, FRANCESCO ALESSANDRO / Sales Pardo, Marta
    Keywords: Perturbed systems Inference Complex networks Belief propagation inference complex networks belief propagation
    Abstract: 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
    Thematic Areas: 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
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 23752548
    Author's mail: roger.guimera@urv.cat jorge.joven@urv.cat marta.sales@urv.cat raul.beltran@urv.cat
    Author identifier: 0000-0002-3597-4310 0000-0003-2749-4541 0000-0002-8140-6525 0000-0001-9691-1906
    Record's date: 2024-10-19
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://advances.sciencemag.org/content/2/10/e1501638
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Science Advances. 2 (10): e1501638-
    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
    Article's DOI: 10.1126/sciadv.1501638
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2016
    Publication Type: Journal Publications
  • Keywords:

    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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