Detection of timescales in evolving complex systems

Tutkimustuotos: Lehtiartikkeli

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Detection of timescales in evolving complex systems. / Darst, Richard; Granell, Clara; Arenas, Alex; Gómez, Sergio; Saramäki, Jari; Fortunato, Santo.

julkaisussa: Scientific Reports, Vuosikerta 6, 39713 , 22.12.2016, s. 1-8.

Tutkimustuotos: Lehtiartikkeli

Harvard

Darst, R, Granell, C, Arenas, A, Gómez, S, Saramäki, J & Fortunato, S 2016, 'Detection of timescales in evolving complex systems', Scientific Reports, Vuosikerta. 6, 39713 , Sivut 1-8. https://doi.org/10.1038/srep39713

APA

Darst, R., Granell, C., Arenas, A., Gómez, S., Saramäki, J., & Fortunato, S. (2016). Detection of timescales in evolving complex systems. Scientific Reports, 6, 1-8. [39713 ]. https://doi.org/10.1038/srep39713

Vancouver

Author

Darst, Richard ; Granell, Clara ; Arenas, Alex ; Gómez, Sergio ; Saramäki, Jari ; Fortunato, Santo. / Detection of timescales in evolving complex systems. Julkaisussa: Scientific Reports. 2016 ; Vuosikerta 6. Sivut 1-8.

Bibtex - Lataa

@article{cf6742f9fe9b4b51ae83ba51621c8936,
title = "Detection of timescales in evolving complex systems",
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.",
author = "Richard Darst and Clara Granell and Alex Arenas and Sergio G{\'o}mez and Jari Saram{\"a}ki and Santo Fortunato",
year = "2016",
month = "12",
day = "22",
doi = "10.1038/srep39713",
language = "English",
volume = "6",
pages = "1--8",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",

}

RIS - Lataa

TY - JOUR

T1 - Detection of timescales in evolving complex systems

AU - Darst, Richard

AU - Granell, Clara

AU - Arenas, Alex

AU - Gómez, Sergio

AU - Saramäki, Jari

AU - Fortunato, Santo

PY - 2016/12/22

Y1 - 2016/12/22

N2 - 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.

AB - 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.

U2 - 10.1038/srep39713

DO - 10.1038/srep39713

M3 - Article

VL - 6

SP - 1

EP - 8

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

M1 - 39713

ER -

ID: 9835512