Cristina Masoller
Systems composed by interacting dynamical elements are ubiquitous in nature. In many situations, such systems are modeled as networks of simple oscillators, where the nodes represent the individual units and the links represent the interactions among them. These interactions are often unknown, and a popular method for inferring the underlying connectivity of a system (i.e., the set of links among pairs of nodes) is based on a statistical similarity analysis of the time-series collected from the dynamics of the nodes. In this presentation I will discuss our recent work on inferring network connectivity from observed data. First, I will consider synthetic data and experimental data generated from simple dynamical units (Logistic maps, Kuramoto phase oscillators and Rössler electronic oscillators), which are coupled with known network topology [1, 2]. I will show that, under adequate conditions, the coupling links can be perfectly inferred, i.e., no mistakes are made regarding the presence or absence of links. Then, I will present ongoing work in assessing climate interactions from the analysis of observed climatological data (surface air temperature), recorded at a regular grid of geographical locations covering the Earth surface
[1] N. Rubido, A. C. Marti, E. Bianco-Martinez, C. Grebogi, M. S. Baptista and C. Masoller, "Exact detection of direct links in networks of coupled maps", New Journal of Physics 16 093010 (2014).
[2] G. Tirabassi, R. Sevilla-Escoboza, J. M. Buldú and C. Masoller, “Inferring the connectivity of coupled oscillators from time-series statistical similarity analysis�, Sci. Rep. 5 10829 (2015).
[3] J. I. Deza, M. Barreiro, and C. Masoller, “Assessing the direction of climate interactions by means of complex networks and information theoretic tools�, Chaos 25, 033105 (2015).
