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Inferring the connectivity of coupled oscillators from time-series statistical similarity analysis
dc.contributor.author | Tirabassi, Giulio |
dc.contributor.author | Sevilla Escoboza, Ricardo |
dc.contributor.author | Martín Buldú, Javier |
dc.contributor.author | Masoller Alonso, Cristina |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Física |
dc.date.accessioned | 2016-01-15T14:51:36Z |
dc.date.available | 2016-01-15T14:51:36Z |
dc.date.issued | 2015-06-04 |
dc.identifier.citation | Tirabassi, 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.issn | 2045-2322 |
dc.identifier.uri | http://hdl.handle.net/2117/81536 |
dc.description.abstract | 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 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.extent | 14 p. |
dc.language.iso | eng |
dc.publisher | Macmillan Publishers |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Física |
dc.subject | Àrees temàtiques de la UPC::Informàtica |
dc.subject.lcsh | Complex networks and dynamic systems |
dc.subject.lcsh | Mathematical models |
dc.subject.other | climate networks |
dc.subject.other | synchronization |
dc.subject.other | fluctuations |
dc.subject.other | complex networks |
dc.subject.other | information theory and computation |
dc.title | Inferring the connectivity of coupled oscillators from time-series statistical similarity analysis |
dc.type | Article |
dc.subject.lemac | Sistemes complexos |
dc.subject.lemac | Models matemàtics |
dc.contributor.group | Universitat Politècnica de Catalunya. DONLL - Dinàmica no Lineal, Òptica no Lineal i Làsers |
dc.identifier.doi | 10.1038/srep10829 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://www.nature.com/articles/srep10829 |
dc.rights.access | Open Access |
local.identifier.drac | 16632762 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO/6PN/FIS2012-37655-C02-01 |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/FP7/289447/EU/Learning about Interacting Networks in Climate/LINC |
local.citation.author | Tirabassi, G.; Sevilla, R.; Martín Buldú, J.; Masoller, C. |
local.citation.publicationName | Scientific reports |
local.citation.volume | 5 |
local.citation.number | 10829 |
local.citation.startingPage | 1 |
local.citation.endingPage | 14 |
dc.identifier.pmid | 26042395 |
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