Impact of lag information on network inference
Instituto de Física de la Facultad de Ciencias (IFFC), Universidad de la República, Iguá 4225,
2 Departament de Física, Universitat Politécnica de Catalunya, Rambla St. Nebridi 22, 08222 Terrassa, Barcelona, Spain
a e-mail: email@example.com
Received in final form: 4 June 2018
Published online: 12 December 2018
Extracting useful information from data is a fundamental challenge across disciplines as diverse as climate, neuroscience, genetics, and ecology. In the era of “big data,” data is ubiquitous, but appropriate methods are needed for gaining reliable information from the data. In this work, we consider a complex system, composed by interacting units, and aim at inferring which elements influence each other, directly from the observed data. The only assumption about the structure of the system is that it can be modeled by a network composed by a set of N units connected with L un-weighted and un-directed links, however, the structure of the connections is not known. In this situation, the inference of the underlying network is usually done by using interdependency measures, computed from the output signals of the units. We show, using experimental data recorded from randomly coupled electronic Rössler chaotic oscillators, that the information of the lag times obtained from bivariate cross-correlation analysis can be useful to gain information about the real connectivity of the system.
© EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature, 2018