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Clustering determines the dynamics of complex contagions in multiplex networks

  • Datos identificativos

    Identificador: PC:2546
    Autores:
    Alex ArenasYong ZhuangOsman Yagan
    Resumen:
    DOI: 10.1103/PhysRevE.95.012312 URL: http://journals.aps.org/pre/abstract/10.1103/PhysRevE.95.012312 Filiació URV: SI Inclòs a la memòria: SI
  • Otros:

    Autor según el artículo: Alex Arenas; Yong Zhuang; Osman Yagan
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: ARENAS MORENO, ALEJANDRO; Yong Zhuang; Osman Yagan
    Palabras clave: Multiplexing Dynamics Complex networks
    Resumen: We present the mathematical analysis of generalized complex contagions in a class of clustered multiplex networks. The model is intended to understand spread of influence, or any other spreading process implying a threshold dynamics, in setups of interconnected networks with significant clustering. The contagion is assumed to be general enough to account for a content-dependent linear threshold model, where each link type has a different weight (for spreading influence) that may depend on the content (e.g., product, rumor, political view) that is being spread. Using the generating functions formalism, we determine the conditions, probability, and expected size of the emergent global cascades. This analysis provides a generalization of previous approaches and is especially useful in problems related to spreading and percolation. The results present nontrivial dependencies between the clustering coefficient of the networks and its average degree. In particular, several phase transitions are shown to occur depending on these descriptors. Generally speaking, our findings reveal that increasing clustering decreases the probability of having global cascades and their size, however, this tendency changes with the average degree. There exists a certain average degree from which on clustering favors the probability and size of the contagion. By comparing the dynamics of complex contagions over multiplex networks and their monoplex projections, we demonstrate that ignoring link types and aggregating network layers may lead to inaccurate conclusions about contagion dynamics, particularly when the correlation of degrees between layers is high.
    Grupo de investigación: Algorithms embedded in Physical Systems
    Áreas temáticas: Computer engineering Ingeniería informática Enginyeria informàtica
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 1539-3755
    Identificador del autor: 0000-0003-0937-0334; n/a; n/a
    Fecha de alta del registro: 2017-02-08
    Volumen de revista: 95
    Versión del articulo depositado: info:eu-repo/semantics/acceptedVersion
    Enlace a la fuente original: https://journals.aps.org/pre/abstract/10.1103/PhysRevE.95.012312
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.1103/PhysRevE.95.012312
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2017
    Página inicial: Art.num. 012312
    Tipo de publicación: Article Artículo Article
  • Palabras clave:

    Anàlisi matemàtica
    Xarxes complexes
    Multiplexing
    Dynamics
    Complex networks
    Computer engineering
    Ingeniería informática
    Enginyeria informàtica
    1539-3755
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