Inferring directed networks using a rank-based connectivity measure

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 d...

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Bibliographic Details
Published in:Physical review. E 2019-01, Vol.99 (1-1), p.012319-012319, Article 012319
Main Authors: Leguia, Marc G, Martínez, Cristina G B, Malvestio, Irene, Campo, Adrià Tauste, Rocamora, Rodrigo, Levnajić, Zoran, Andrzejak, Ralph G
Format: Article
Language:English
Subjects:
Chaotic systems
Collective dynamics
Directed networks
Electroencephalography
Networks
Nonlinear Dynamics
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Summary: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.
ISSN:2470-0053
2470-0045
2470-0053
DOI:10.1103/PhysRevE.99.012319