Causation Entropy En Route to Re-Synchronization Unfolds the Structure of Chaotic Dynamical Networks

Revealing the interactions between the individual components in complex networks utilizing limited observations is a subject of scientific interest. Determination of connectivity of the network is important, specifically. When the network consists of dynamically evolving nodes that tend to synchronize, the problem is especially hard because the observations from synchronizing nodes are identical eventually and do not reveal much about the structure. Here, we present a method for reconstructing unidirectional chaotic synchronized networks based on average integrated causation entropy. We show that if we inject random information via impulsive perturbations into the individual systems to destroy the synchronization briefly, that can lead to the prediction of the network structure en route to re-synchronization. We propose an algorithm based on the properties of average integrated causation entropy, a measure of information transferred between two nodes relative to a third node. The performance of the proposed algorithm has been demonstrated with chaotic networks to show that it can accurately reconstruct the structure of chaotic dynamical systems.