Articles producció científica> Enginyeria Informàtica i Matemàtiques

Detection of timescales in evolving complex systems

  • Identification data

    Identifier: imarina:9282629
    Authors:
    Darst, Richard KGranell, ClaraArenas, AlexGomez, SergioSaramaki, JariFortunato, Santo
    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. © The Author(s) 2016.
  • Others:

    Author, as appears in the article.: Darst, Richard K; Granell, Clara; Arenas, Alex; Gomez, Sergio; Saramaki, Jari; Fortunato, Santo
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Arenas Moreno, Alejandro / Gómez Jiménez, Sergio
    Keywords: Model
    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. © The Author(s) 2016.
    Thematic Areas: Zootecnia / recursos pesqueiros Saúde coletiva Química Psicología Odontología Nutrição Multidisciplinary sciences Multidisciplinary Medicina veterinaria Medicina iii Medicina ii Medicina i Materiais Matemática / probabilidade e estatística Letras / linguística Interdisciplinar Geografía Geociências Farmacia Engenharias iv Engenharias iii Engenharias ii Enfermagem Educação física Educação Economia Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências ambientais Ciências agrárias i Ciência de alimentos Ciência da computação Biotecnología Biodiversidade Astronomia / física
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: sergio.gomez@urv.cat alexandre.arenas@urv.cat
    Author identifier: 0000-0003-1820-0062 0000-0003-0937-0334
    Record's date: 2024-09-28
    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/
    Papper original source: Scientific Reports. 6 39713-
    APA: Darst, Richard K; Granell, Clara; Arenas, Alex; Gomez, Sergio; Saramaki, Jari; Fortunato, Santo (2016). Detection of timescales in evolving complex systems. Scientific Reports, 6(), 39713-. DOI: 10.1038/srep39713
    Article's DOI: 10.1038/srep39713
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2016
    Publication Type: Journal Publications
  • Keywords:

    Multidisciplinary,Multidisciplinary Sciences
    Model
    Zootecnia / recursos pesqueiros
    Saúde coletiva
    Química
    Psicología
    Odontología
    Nutrição
    Multidisciplinary sciences
    Multidisciplinary
    Medicina veterinaria
    Medicina iii
    Medicina ii
    Medicina i
    Materiais
    Matemática / probabilidade e estatística
    Letras / linguística
    Interdisciplinar
    Geografía
    Geociências
    Farmacia
    Engenharias iv
    Engenharias iii
    Engenharias ii
    Enfermagem
    Educação física
    Educação
    Economia
    Ciências biológicas iii
    Ciências biológicas ii
    Ciências biológicas i
    Ciências ambientais
    Ciências agrárias i
    Ciência de alimentos
    Ciência da computação
    Biotecnología
    Biodiversidade
    Astronomia / física
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