How complex a complex network of equal nodes can be?

TL;DR

This paper investigates the complexity of active networks by analyzing Lyapunov exponents, showing that the network's chaos can be bounded and predicted using simpler two-node systems, emphasizing the role of interactions over topology.

Contribution

It introduces a bound on the sum of positive Lyapunov exponents for active networks, linking network complexity to the properties of a two-node system, simplifying analysis.

Findings

Sum of positive Lyapunov exponents is bounded by that of the synchronization manifold.

Interactions influence information production more than network topology.

Behavior of large networks can be predicted from two-node systems.

Abstract

Positive Lyapunov exponents measure the asymptotic exponential divergence of nearby trajectories of a dynamical system. Not only they quantify how chaotic a dynamical system is, but since their sum is an upper bound for the entropy by the Ruelle inequality, they also provide a convenient way to quantify the complexity of an active network. We present numerical evidences that for a large class of active networks, the sum of the positive Lyapunov exponents is bounded by the sum of the positive Lyapunov exponents of the corresponding synchronization manifold, the last quantity being in principle easier to compute than the latter. This fact is a consequence of the property that for an active network considered here, the amount of information produced is more affected by the interactions between the nodes than by the topology of the network. Using the inequality described above, we explain how to predict the behavior of a large active network only knowing the information provided by an active network consisting of two coupled nodes.