Cluster Synchronization via Graph Laplacian Eigenvectors
By: Tobias Timofeyev, Alice Patania
Potential Business Impact:
Helps groups of things sync up better.
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 a relaxation of an AEP we call a quasi-equitable partition at level $\delta$ ($\delta-$QEP). $\delta-$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.
Similar Papers
Koopman Invariants as Drivers of Emergent Time-Series Clustering in Joint-Embedding Predictive Architectures
Machine Learning (CS)
Helps AI understand patterns in changing data.
An Improved and Generalised Analysis for Spectral Clustering
Machine Learning (CS)
Finds hidden groups in connected information.
Stable Computation of Laplacian Eigenfunctions Corresponding to Clustered Eigenvalues
Spectral Theory
Finds shapes of things when they are very close together.