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A framework for the construction of generative models for mesoscale structure in multilayer networks

  • Identification data

    Identifier: imarina:9381082
    Handle: https://hdl.handle.net/20.500.11797/imarina9381082
    Authors:
    Bazzi, MaryaJeub, Lucas G SArenas, AlexHowison, Sam DPorter, Mason A
    Abstract:
    Multilayer networks allow one to represent diverse and coupled connectivity patterns-such as time-dependence, multiple subsystems, or both-that arise in many applications and which are difficult or awkward to incorporate into standard network representations. In the study of multilayer networks, it is important to investigate mesoscale (i.e., intermediate-scale) structures, such as dense sets of nodes known as communities, to discover network features that are not apparent at the microscale or the macroscale. The ill-defined nature of mesoscale structure and its ubiquity in empirical networks make it crucial to develop generative models that can produce the features that one encounters in empirical networks. Key purposes of such models include generating synthetic networks with empirical properties of interest, benchmarking mesoscale-detection methods and algorithms, and inferring structure in empirical multilayer networks. In this paper, we introduce a framework for the construction of generative models for mesoscale structures in multilayer networks. Our framework provides a standardized set of generative models, together with an associated set of principles from which they are derived, for studies of mesoscale structures in multilayer networks. It unifies and generalizes many existing models for mesoscale structures in fully ordered (e.g., temporal) and unordered (e.g., multiplex) multilayer networks. One can also use it to construct generative models for mesoscale structures in partially ordered multilayer networks (e.g., networks that are both temporal and multiplex). Our framework has the ability to produce many features of empirical multilayer networks, and it explicitly incorporates a user-specified dependency structure between layers. We discuss the parameters

  • Others:

    Author, as appears in the article.: Bazzi, Marya; Jeub, Lucas G S; Arenas, Alex; Howison, Sam D; Porter, Mason A
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Arenas Moreno, Alejandro
    Keywords: Blockmodels Community structure Compatibility Complex networks Information Markov-chains Multiscal Prediction Random-walks Stochastic block-models
    Abstract: Multilayer networks allow one to represent diverse and coupled connectivity patterns-such as time-dependence, multiple subsystems, or both-that arise in many applications and which are difficult or awkward to incorporate into standard network representations. In the study of multilayer networks, it is important to investigate mesoscale (i.e., intermediate-scale) structures, such as dense sets of nodes known as communities, to discover network features that are not apparent at the microscale or the macroscale. The ill-defined nature of mesoscale structure and its ubiquity in empirical networks make it crucial to develop generative models that can produce the features that one encounters in empirical networks. Key purposes of such models include generating synthetic networks with empirical properties of interest, benchmarking mesoscale-detection methods and algorithms, and inferring structure in empirical multilayer networks. In this paper, we introduce a framework for the construction of generative models for mesoscale structures in multilayer networks. Our framework provides a standardized set of generative models, together with an associated set of principles from which they are derived, for studies of mesoscale structures in multilayer networks. It unifies and generalizes many existing models for mesoscale structures in fully ordered (e.g., temporal) and unordered (e.g., multiplex) multilayer networks. One can also use it to construct generative models for mesoscale structures in partially ordered multilayer networks (e.g., networks that are both temporal and multiplex). Our framework has the ability to produce many features of empirical multilayer networks, and it explicitly incorporates a user-specified dependency structure between layers. We discuss the parameters and properties of our framework, and we illustrate examples of its use with benchmark models for community-detection methods and algorithms in multilayer networks.
    Thematic Areas: Physics and astronomy (all) Physics and astronomy (miscellaneous) Physics, multidisciplinary
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: alexandre.arenas@urv.cat
    Author identifier: 0000-0003-0937-0334
    Record's date: 2024-09-28
    Papper version: info:eu-repo/semantics/publishedVersion
    Papper original source: Physical Review Research. 2 (2): 023100-
    APA: Bazzi, Marya; Jeub, Lucas G S; Arenas, Alex; Howison, Sam D; Porter, Mason A (2020). A framework for the construction of generative models for mesoscale structure in multilayer networks. Physical Review Research, 2(2), 023100-. DOI: 10.1103/physrevresearch.2.023100
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2020
    Publication Type: Journal Publications

  • Keywords:

    Physics and Astronomy (Miscellaneous),Physics, Multidisciplinary
    Blockmodels
    Community structure
    Compatibility
    Complex networks
    Information
    Markov-chains
    Multiscal
    Prediction
    Random-walks
    Stochastic block-models
    Physics and astronomy (all)
    Physics and astronomy (miscellaneous)
    Physics, multidisciplinary

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