Modules as effective nodes in coarse-grained networks of Kuramoto oscillators

TL;DR

This paper introduces a coarse-graining method for modular networks of Kuramoto oscillators, simplifying analysis by replacing modules with effective nodes, and demonstrates its accuracy in predicting synchronization behavior.

Contribution

The work presents a novel coarse-graining procedure inspired by EEG data, enabling simplified analysis of complex modular oscillator networks while preserving key dynamical features.

Findings

The method accurately predicts phase transitions in networks with 2 and 3 modules.

It remains effective for real networks with small modularity coefficients.

The approach can infer module synchrony even with low individual module synchronizability.

Abstract

Most real-world networks exhibit a significant degree of modularity. Understanding the effects of such topology on dynamical processes is pivotal for advances in social and natural sciences. In this work we consider the dynamics of Kuramoto oscillators on modular networks and propose a simple coarse-graining procedure where modules are replaced by effective single oscillators. The method is inspired by EEG measurements, where very large groups of neurons under each electrode are interpreted as single nodes in a correlation network. We expose the interplay between intra-module and inter-module coupling strengths in keeping the coarse-graining process meaningful. We show that, when modules are well synchronized, the phase transition from asynchronous to synchronous motion in networks with 2 and 3 modules is very well described by their respective reduced systems, regardless of the network structure connecting the modules. Applications of the method to real networks with small modularity coefficients reveals that the approximation is also very accurate if oscillators in each module are identical. The method reproduces global synchronization patterns despite the low synchronizability of some modules, possibly allowing for the inference of the mean synchrony of each module when individual dynamics are not known.