Inferring directed networks using a rank-based connectivity measure

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• dc.contributor.author Grau Leguia, Marc
• dc.contributor.author Martínez, Cristina G. B.
• dc.contributor.author Malvestio, Irene
• dc.contributor.author Tauste Campo, Adrià, 1982-
• dc.contributor.author Rocamora, Rodrigo
• dc.contributor.author Levnajić, Zoran
• dc.contributor.author Andrzejak, Ralph Gregor
• dc.date.accessioned 2019-06-18T15:29:36Z
• dc.date.available 2019-06-18T15:29:36Z
• dc.date.issued 2019
• dc.description.abstract Inferring the topology of a network using the knowledge of the signals of each of the interacting units is key to understanding real-world systems. One way to address this problem is using data- driven methods like cross-correlation or mutual information. However, these measures lack the ability to distinguish the direction of coupling. Here, we use a rank-based nonlinear interdependence measure originally developed for pairs of signals. This measure not only allows one to measure the strength but also the direction of the coupling. Our results for a system of coupled Lorenz dynamics show that we are able to consistently infer the underlying network for a subrange of the coupling strength and link density. Furthermore, we report that the addition of dynamical noise can benefit the reconstruction. Finally, we show an application to multichannel electroencephalographic recordings from an epilepsy patient.en
• dc.description.sponsorship This work was funded by the EU via H2020 Marie SklodowskaCurie project COSMOS, grant no. 642563 (M.G.L., I.M., Z.L., and R.G.A). R.G.A. and C.G.B.M. acknowledge funding from the Spanish Ministry of Econ- omy and Competitiveness (Grant FIS2014-54177-R) and the CERCA Programme of the Generalitat de Catalunya. C.G.B.M. acknowledges the support by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502). Z.L. acknowledges funding from the Slovenian Re- search Agency via program Complex Networks P1-0383 and project J5-8236.
• dc.format.mimetype application/pdf
• dc.identifier.citation Leguia MG, Martínez CGB, Malvestio I, Tauste Campo A, Rocamora R, Levnajić Z, Andrzejak RG. Inferring directed networks using a rank-based connectivity measure. Phys Rev E.2019 Jan 22;99(1):012319. DOI: 10.1103/PhysRevE.99.012319
• dc.identifier.doi http://dx.doi.org/10.1103/PhysRevE.99.012319
• dc.identifier.issn 2470-0045
• dc.identifier.uri http://hdl.handle.net/10230/41825
• dc.language.iso eng
• dc.publisher American Physical Society
• dc.relation.ispartof Physical Review E. 2019 Jan 22;99(1):012319
• dc.relation.isreferencedby http://hdl.handle.net/10230/42764
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/642563
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/FIS2014-54177-R
• dc.rights © American Physical Society. Published article available at https://dx.doi.org/10.1103/PhysRevE.99.012319
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Chaotic systemsen
• dc.subject.keyword Collective dynamicsen
• dc.subject.keyword Directed networksen
• dc.subject.keyword Electroencephalographyen
• dc.subject.keyword Nonlinear Dynamicsen
• dc.subject.keyword Networksen
• dc.title Inferring directed networks using a rank-based connectivity measure
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion