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dc.contributor.authorTirabassi, Giulio
dc.contributor.authorSevilla Escoboza, Ricardo
dc.contributor.authorMartín Buldú, Javier
dc.contributor.authorMasoller Alonso, Cristina
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Física
dc.date.accessioned2016-01-15T14:51:36Z
dc.date.available2016-01-15T14:51:36Z
dc.date.issued2015-06-04
dc.identifier.citationTirabassi, G., Sevilla, R., Martín Buldú, J., Masoller, C. Inferring the connectivity of coupled oscillators from time-series statistical similarity analysis. "Scientific reports", 04 Juny 2015, vol. 5, núm. 10829, p. 1-14.
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/2117/81536
dc.description.abstractA 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 Rossler 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.
dc.format.extent14 p.
dc.language.isoeng
dc.publisherMacmillan Publishers
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Física
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshComplex networks and dynamic systems
dc.subject.lcshMathematical models
dc.subject.otherclimate networks
dc.subject.othersynchronization
dc.subject.otherfluctuations
dc.subject.othercomplex networks
dc.subject.otherinformation theory and computation
dc.titleInferring the connectivity of coupled oscillators from time-series statistical similarity analysis
dc.typeArticle
dc.subject.lemacSistemes complexos
dc.subject.lemacModels matemàtics
dc.contributor.groupUniversitat Politècnica de Catalunya. DONLL - Dinàmica no lineal, òptica no lineal i làsers
dc.identifier.doi10.1038/srep10829
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.nature.com/articles/srep10829
dc.rights.accessOpen Access
drac.iddocument16632762
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/PN2008-2011/FIS2012-37655-C02-01
upcommons.citation.authorTirabassi, G., Sevilla, R., Martín Buldú, J., Masoller, C.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameScientific reports
upcommons.citation.volume5
upcommons.citation.number10829
upcommons.citation.startingPage1
upcommons.citation.endingPage14
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