Articles producció científica> Enginyeria Informàtica i Matemàtiques

Detection of timescales in evolving complex systems

  • Datos identificativos

    Identificador: PC:2482
    Autores:
    Alex ArenasRichard K. DarstClara GranellJari SaramäkiSergio GómezSanto Fortunato
    Resumen:
    Most complex systems are intrinsically dynamic in nature. The evolution of a dynamic complex system is typically represented as a sequence of snapshots, where each snapshot describes the configuration of the system at a particular instant of time. This is often done by using constant intervals but a better approach would be to define dynamic intervals that match the evolution of the system's configuration. To this end, we propose a method that aims at detecting evolutionary changes in the configuration of a complex system, and generates intervals accordingly. We show that evolutionary timescales can be identified by looking for peaks in the similarity between the sets of events on consecutive time intervals of data. Tests on simple toy models reveal that the technique is able to detect evolutionary timescales of time-varying data both when the evolution is smooth as well as when it changes sharply. This is further corroborated by analyses of several real datasets. Our method is scalable to extremely large datasets and is computationally efficient. This allows a quick, parameter-free detection of multiple timescales in the evolution of a complex system.
  • Otros:

    Autor según el artículo: Alex Arenas; Richard K. Darst; Clara Granell; Jari Saramäki; Sergio Gómez; Santo Fortunato
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: ARENAS MORENO, ALEJANDRO; Richard K. Darst; Clara Granell; Jari Saramäki; GÓMEZ JIMÉNEZ, SERGIO; Santo Fortunato
    Palabras clave: Human dynamics model Networks
    Resumen: Most complex systems are intrinsically dynamic in nature. The evolution of a dynamic complex system is typically represented as a sequence of snapshots, where each snapshot describes the configuration of the system at a particular instant of time. This is often done by using constant intervals but a better approach would be to define dynamic intervals that match the evolution of the system's configuration. To this end, we propose a method that aims at detecting evolutionary changes in the configuration of a complex system, and generates intervals accordingly. We show that evolutionary timescales can be identified by looking for peaks in the similarity between the sets of events on consecutive time intervals of data. Tests on simple toy models reveal that the technique is able to detect evolutionary timescales of time-varying data both when the evolution is smooth as well as when it changes sharply. This is further corroborated by analyses of several real datasets. Our method is scalable to extremely large datasets and is computationally efficient. This allows a quick, parameter-free detection of multiple timescales in the evolution of a complex system.
    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: 2045-2322
    Identificador del autor: 0000-0003-0937-0334; n/a; n/a; n/a; 0000-0003-1820-0062; n/a
    Fecha de alta del registro: 2017-01-17
    Volumen de revista: 6
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.nature.com/articles/srep39713
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.1038/srep39713
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2016
    Página inicial: Art.num. 39713
    Tipo de publicación: Article Artículo Article
  • Palabras clave:

    Anàlisi de sistemes
    Human dynamics
    model
    Networks
    Computer engineering
    Ingeniería informática
    Enginyeria informàtica
    2045-2322
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