Articles producció científica> Enginyeria Informàtica i Matemàtiques

Detection of timescales in evolving complex systems

  • Identification data

    Identifier: PC:2482
    Authors:
    Alex ArenasRichard K. DarstClara GranellJari SaramäkiSergio GómezSanto Fortunato
    Abstract:
    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.
  • Others:

    Author, as appears in the article.: Alex Arenas; Richard K. Darst; Clara Granell; Jari Saramäki; Sergio Gómez; Santo Fortunato
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: ARENAS MORENO, ALEJANDRO; Richard K. Darst; Clara Granell; Jari Saramäki; GÓMEZ JIMÉNEZ, SERGIO; Santo Fortunato
    Keywords: Human dynamics model Networks
    Abstract: 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.
    Research group: Algorithms embedded in Physical Systems
    Thematic Areas: Computer engineering Ingeniería informática Enginyeria informàtica
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 2045-2322
    Author identifier: 0000-0003-0937-0334; n/a; n/a; n/a; 0000-0003-1820-0062; n/a
    Record's date: 2017-01-17
    Journal volume: 6
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.nature.com/articles/srep39713
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Article's DOI: 10.1038/srep39713
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2016
    First page: Art.num. 39713
    Publication Type: Article Artículo Article
  • Keywords:

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

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