Detection of timescales in evolving complex systems

Richard Darst, Clara Granell, Alex Arenas, Sergio Gómez, Jari Saramäki, Santo Fortunato

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)
146 Downloads (Pure)

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.
Original languageEnglish
Article number39713
Pages (from-to)1-8
Number of pages8
JournalScientific Reports
Volume6
DOIs
Publication statusPublished - 22 Dec 2016
MoE publication typeA1 Journal article-refereed

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