Dissimilarity between synchronization processes on networks

TL;DR

This paper introduces a general framework for comparing synchronization processes on networks using a dissimilarity measure, enabling analysis of how network structure influences oscillator synchronization.

Contribution

It presents a novel metric-based method for comparing dynamical synchronization processes on networks, applicable to various models including Kuramoto and its linear approximation.

Findings

Analyzed the impact of edge weights and network modifications on synchronization.

Compared synchronization in different graph structures, including nonisomorphic graphs.

Contrasted Kuramoto model behavior with its linear approximation across network types.

Abstract

In this study, we present a general framework for comparing two dynamical processes that describe the synchronization of oscillators coupled through networks of the same size. We introduce a measure of dissimilarity defined in terms of a metric on a hypertorus, allowing us to compare the phases of coupled oscillators. In the first part, this formalism is implemented to examine systems of networked identical phase oscillators that evolve with the Kuramoto model. In particular, we analyze the effect of the weight of an edge in the synchronization of two oscillators, the introduction of new sets of edges in interacting cycles, the effect of bias in the couplings, and the addition of a link in a ring. We also compare the synchronization of nonisomorphic graphs with four nodes. Finally, we explore the dissimilarities generated when we contrast the Kuramoto model with its linear approximation for different random initial phases in deterministic and random networks. The approach introduced provides a general tool for comparing synchronization processes on networks, allowing us to understand the dynamics of a complex system as a consequence of the coupling structure and the processes that can occur in it.