Articles producció científica> Enginyeria Informàtica i Matemàtiques

Benchmark model to assess community structure in evolving networks

  • Dades identificatives

    Identificador: imarina:9285449
    Autors:
    Granell, ClaraDarst, Richard KArenas, AlexFortunato, SantoGomez, Sergio
    Resum:
    Detecting the time evolution of the community structure of networks is crucial to identify major changes in the internal organization of many complex systems, which may undergo important endogenous or exogenous events. This analysis can be done in two ways: considering each snapshot as an independent community detection problem or taking into account the whole evolution of the network. In the first case, one can apply static methods on the temporal snapshots, which correspond to configurations of the system in short time windows, and match afterward the communities across layers. Alternatively, one can develop dedicated dynamic procedures so that multiple snapshots are simultaneously taken into account while detecting communities, which allows us to keep memory of the flow. To check how well a method of any kind could capture the evolution of communities, suitable benchmarks are needed. Here we propose a model for generating simple dynamic benchmark graphs, based on stochastic block models. In them, the time evolution consists of a periodic oscillation of the system's structure between configurations with built-in community structure. We also propose the extension of quality comparison indices to the dynamic scenario. © 2015 American Physical Society.
  • Altres:

    Autor segons l'article: Granell, Clara; Darst, Richard K; Arenas, Alex; Fortunato, Santo; Gomez, Sergio
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Arenas Moreno, Alejandro / Gómez Jiménez, Sergio / Granell Martorell, Clara
    Paraules clau: Stochastic systems Stochastic models Stochastic block models Social sciences Short time windows Population dynamics Periodic oscillation Evolving networks Dynamic scenarios Computer networks Complex networks Community structures Community detection Benchmark models
    Resum: Detecting the time evolution of the community structure of networks is crucial to identify major changes in the internal organization of many complex systems, which may undergo important endogenous or exogenous events. This analysis can be done in two ways: considering each snapshot as an independent community detection problem or taking into account the whole evolution of the network. In the first case, one can apply static methods on the temporal snapshots, which correspond to configurations of the system in short time windows, and match afterward the communities across layers. Alternatively, one can develop dedicated dynamic procedures so that multiple snapshots are simultaneously taken into account while detecting communities, which allows us to keep memory of the flow. To check how well a method of any kind could capture the evolution of communities, suitable benchmarks are needed. Here we propose a model for generating simple dynamic benchmark graphs, based on stochastic block models. In them, the time evolution consists of a periodic oscillation of the system's structure between configurations with built-in community structure. We also propose the extension of quality comparison indices to the dynamic scenario. © 2015 American Physical Society.
    Àrees temàtiques: Zootecnia / recursos pesqueiros Statistics and probability Statistical and nonlinear physics Saúde coletiva Química Physics, mathematical Physics, fluids & plasmas Odontología Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Interdisciplinar Geociências General medicine Farmacia Engenharias iv Engenharias iii Engenharias ii Educação física Educação Economia Condensed matter physics Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência da computação Biotecnología Biodiversidade Astronomia / física
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: clara.granell@urv.cat sergio.gomez@urv.cat alexandre.arenas@urv.cat
    Identificador de l'autor: 0000-0003-1820-0062 0000-0003-0937-0334
    Data d'alta del registre: 2024-09-28
    Versió de l'article dipositat: info:eu-repo/semantics/acceptedVersion
    Enllaç font original: https://journals.aps.org/pre/abstract/10.1103/PhysRevE.92.012805
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Physical Review e. 92 (1): 012805-
    Referència de l'ítem segons les normes APA: Granell, Clara; Darst, Richard K; Arenas, Alex; Fortunato, Santo; Gomez, Sergio (2015). Benchmark model to assess community structure in evolving networks. Physical Review e, 92(1), 012805-. DOI: 10.1103/PhysRevE.92.012805
    DOI de l'article: 10.1103/PhysRevE.92.012805
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2015
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Condensed Matter Physics,Physics, Fluids & Plasmas,Physics, Mathematical,Statistical and Nonlinear Physics,Statistics and Probability
    Stochastic systems
    Stochastic models
    Stochastic block models
    Social sciences
    Short time windows
    Population dynamics
    Periodic oscillation
    Evolving networks
    Dynamic scenarios
    Computer networks
    Complex networks
    Community structures
    Community detection
    Benchmark models
    Zootecnia / recursos pesqueiros
    Statistics and probability
    Statistical and nonlinear physics
    Saúde coletiva
    Química
    Physics, mathematical
    Physics, fluids & plasmas
    Odontología
    Medicina ii
    Medicina i
    Materiais
    Matemática / probabilidade e estatística
    Interdisciplinar
    Geociências
    General medicine
    Farmacia
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Educação física
    Educação
    Economia
    Condensed matter physics
    Ciências biológicas ii
    Ciências biológicas i
    Ciências ambientais
    Ciências agrárias i
    Ciência da computação
    Biotecnología
    Biodiversidade
    Astronomia / física
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