Eigenvector-based analysis of cluster synchronization in general complex networks of coupled chaotic oscillators

TL;DR

This paper introduces an eigenvector-based method to identify symmetries and analyze synchronization patterns in large complex networks of coupled chaotic oscillators, improving understanding of cluster synchronization.

Contribution

The paper presents a novel eigenvector-based approach for detecting symmetries and studying synchronization transitions in complex networks, including weighted and asymmetric cases.

Findings

Method effectively identifies symmetries in artificial and empirical networks.

Predictions of synchronization clusters align with direct simulations.

Approach extends to weighted networks without strict symmetries.

Abstract

While symmetry has been exploited to analyze synchronization patterns in complex networks, the identification of symmetries in large-size network remains as a challenge. We present in the present work a new method, namely the method of eigenvector-based analysis, to identify symmetries in general complex networks and, incorporating the method of eigenvalue analysis, investigate the formation and transition of synchronization patterns. The efficiency of the proposed method is validated by both artificial and empirical network models consisting of coupled chaotic oscillators. Furthermore, we generalize the method to networks of weighted couplings in which no strict symmetry exists but synchronization clusters are still well organized, with the predications agreeing with the results of direct simulations. Our study provides a new approach for identifying network symmetries, and paves a way to the investigation of synchronization patterns in large-size complex network.