Cluster synchronization via graph Laplacian eigenvectors

Almost equitable partitions (AEPs) have been linked to cluster synchronization in oscillatory systems, highlighting the importance of structure in collective network dynamics. We provide a general spectral framework that formalizes this connection, showing how eigenvectors associated with AEPs span a subspace of the Laplacian spectrum that governs partition-induced synchronization behavior. This offers a principled reduction of network dynamics, allowing clustered states to be understood in terms of quotient graph projections. Our approach clarifies the conditions under which transient hierarchical clustering and multi-frequency synchronization emerge and connects these dynamical phenomena directly to network symmetry and community structure. In doing so, we bridge a critical gap between static topology and dynamic behavior, namely, the lack of a spectral method for analyzing synchronization in networks that exhibit exact or approximate structural regularity. Perfect AEPs are rare in real-world networks since most have some degree of irregularity or noise. We define relaxation of an AEP we call a quasi-equitable partition at level δ (δ-QEP). δ-QEPs can preserve many of the clustering-relevant properties of AEPs while tolerating structural imperfections and noise. This extension enables us to describe synchronization behavior in more realistic scenarios, where ideal symmetries are rarely present. Our findings have important implications for understanding synchronization patterns in real-world networks, from neural circuits to power grids.

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Publication Details

Journal
Chaos An Interdisciplinary Journal of Nonlinear Science
Published
2025-09-01
DOI
https://doi.org/10.1063/5.0280142
Citations
2
Primary Topic
Nonlinear Dynamics and Pattern Formation
Type
article
Field-Weighted Citation Impact
2.97

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article

Cluster synchronization via graph Laplacian eigenvectors

Tobias Timofeyev, Alice Patania
2 citations
Chaos An Interdisciplinary Journal of Nonlinear Science
Nonlinear Dynamics and Pattern Formation
2.97
article

Cluster synchronization via graph Laplacian eigenvectors

Tobias Timofeyev, Alice Patania
article en
2 citations

Abstract

Almost equitable partitions (AEPs) have been linked to cluster synchronization in oscillatory systems, highlighting the importance of structure in collective network dynamics. We provide a general spectral framework that formalizes this connection, showing how eigenvectors associated with AEPs span a subspace of the Laplacian spectrum that governs partition-induced synchronization behavior. This offers a principled reduction of network dynamics, allowing clustered states to be understood in terms of quotient graph projections. Our approach clarifies the conditions under which transient hierarchical clustering and multi-frequency synchronization emerge and connects these dynamical phenomena directly to network symmetry and community structure. In doing so, we bridge a critical gap between static topology and dynamic behavior, namely, the lack of a spectral method for analyzing synchronization in networks that exhibit exact or approximate structural regularity. Perfect AEPs are rare in real-world networks since most have some degree of irregularity or noise. We define relaxation of an AEP we call a quasi-equitable partition at level δ (δ-QEP). δ-QEPs can preserve many of the clustering-relevant properties of AEPs while tolerating structural imperfections and noise. This extension enables us to describe synchronization behavior in more realistic scenarios, where ideal symmetries are rarely present. Our findings have important implications for understanding synchronization patterns in real-world networks, from neural circuits to power grids.

Chaos An Interdisciplinary Journal of Nonlinear ScienceVol. 35(9)
University of Vermont (US)
National Science Foundation
Openalex Percentile: Top 7%
Nonlinear Dynamics and Pattern Formation
2.97
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Cluster synchronization via graph Laplacian eigenvectors — Tobias Timofeyev, Alice Patania · Chaos An Interdisciplinary Journal of Nonlinear Science (2025) | TGRS Research Map | TGRS