Node differentiation dynamics along the route to synchronization in complex networks

TL;DR

This paper investigates how individual nodes in complex networks differentiate dynamically during the route to synchronization, revealing degree-dependent complexity changes and hierarchical differentiation patterns.

Contribution

It introduces a detailed analysis of node-level dynamical differentiation during synchronization, highlighting degree-based patterns and their dependence on coupling functions.

Findings

Nodes of the same degree differentiate similarly during synchronization.

Higher connectivity nodes show more pronounced complexity reduction just after interaction begins.

Hierarchical differentiation occurs with lower degree nodes exhibiting larger complexity changes before full synchronization.

Abstract

Synchronization has been the subject of intense research during decades mainly focused on determining the structural and dynamical conditions driving a set of interacting units to a coherent state globally stable. However, little attention has been paid to the description of the dynamical development of each individual networked unit in the process towards the synchronization of the whole ensemble. In this paper, we show how in a network of identical dynamical systems, nodes belonging to the same degree class differentiate in the same manner visiting a sequence of states of diverse complexity along the route to synchronization independently on the global network structure. In particular, we observe, just after interaction starts pulling orbits from the initially uncoupled attractor, a general reduction of the complexity of the dynamics of all units being more pronounced in those with higher connectivity. In the weak coupling regime, when synchronization starts to build up, there is an increase in the dynamical complexity whose maximum is achieved, in general, first in the hubs due to their earlier synchronization with the mean field. For very strong coupling, just before complete synchronization, we found a hierarchical dynamical differentiation with lower degree nodes being the ones exhibiting the largest complexity departure. We unveil how this differentiation route holds for several models of nonlinear dynamics including toroidal chaos and how it depends on the coupling function. This study provides new insights to understand better strategies for network identification and control or to devise effective methods for network inference.