Inferring the connectivity of coupled oscillators from time-series statistical similarity analysis

A system composed by interacting dynamical elements can be represented by a network, where the nodes represent the elements that constitute the system and the links account for their interactions, which arise due to a variety of mechanisms and which are often unknown. A popular method for inferring...

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Veröffentlicht in:Scientific reports Jg. 5; H. 1; S. 10829
Hauptverfasser: Tirabassi, Giulio, Sevilla-Escoboza, Ricardo, Buldú, Javier M., Masoller, Cristina
Format: Journal Article Verlag
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 04.06.2015
Nature Publishing Group
Macmillan Publishers
Schlagworte:
639/766/259
639/766/530/2801
climate networks
complex networks
Complex networks and dynamic systems
Connectivity
fluctuations
Física
Humanities and Social Sciences
information theory and computation
Informàtica
Mathematical models
Methods
Models matemàtics
multidisciplinary
Nodes
Oscillators
Science
Similarity measures
Sistemes complexos
Statistics
synchronization
Time series
Àrees temàtiques de la UPC
ISSN:2045-2322, 2045-2322
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Zusammenfassung:A system composed by interacting dynamical elements can be represented by a network, where the nodes represent the elements that constitute the system and the links account for their interactions, which arise due to a variety of mechanisms and which are often unknown. A popular method for inferring the system connectivity (i.e., the set of links among pairs of nodes) is by performing a statistical similarity analysis of the time-series collected from the dynamics of the nodes. Here, by considering two systems of coupled oscillators (Kuramoto phase oscillators and Rössler chaotic electronic oscillators) with known and controllable coupling conditions, we aim at testing the performance of this inference method, by using linear and non linear statistical similarity measures. We find that, under adequate conditions, the network links can be perfectly inferred, i.e., no mistakes are made regarding the presence or absence of links. These conditions for perfect inference require: i) an appropriated choice of the observed variable to be analysed, ii) an appropriated interaction strength and iii) an adequate thresholding of the similarity matrix. For the dynamical units considered here we find that the linear statistical similarity measure performs, in general, better than the non-linear ones.
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ISSN:2045-2322
2045-2322
DOI:10.1038/srep10829