Grau Leguia, MarcMartínez, Cristina G. B.Malvestio, IreneTauste Campo, Adrià, 1982-Rocamora, RodrigoLevnajić, ZoranAndrzejak, Ralph Gregor2019-06-182019-06-182019Leguia 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.99.0123192470-0045http://hdl.handle.net/10230/41825Inferring 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.application/pdfeng© American Physical Society. Published article available at https://dx.doi.org/10.1103/PhysRevE.99.012319info:eu-repo/semantics/articlehttp://dx.doi.org/10.1103/PhysRevE.99.012319Chaotic systemsCollective dynamicsDirected networksElectroencephalographyNonlinear DynamicsNetworksinfo:eu-repo/semantics/openAccess