Graph partitions and cluster synchronization in networks of oscillators
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Abstract (arXiv)
Synchronization over networks depends strongly on the structure of the coupling between the oscillators. When the coupling presents certain regularities, the dynamics can be coarse-grained into clusters by means of External Equitable Partitions of the network graph and their associated quotient graphs. We exploit this graph-theoretical concept to study the phenomenon of cluster synchronization, in which different groups of nodes converge to distinct behaviors. We derive conditions and properties of networks in which such clustered behavior emerges, and show that the ensuing dynamics is the result of the localization of the eigenvectors of the associated graph Laplacians linked to the existence of invariant subspaces. The framework is applied to both linear and non-linear models, first for the standard case of networks with positive edges, before being generalized to the case of signed networks with both positive and negative interactions. We illustrate our results with examples of both signed and unsigned graphs for consensus dynamics and for partial synchronization of oscillator networks under the master stability function as well as Kuramoto oscillators.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Aug 2016.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1.
5 papers.
- 2024Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNscited by 31
- 2021A nonlinear diffusion method for semi-supervised learning on hypergraphscited by 11
- 2021KuraNet: Systems of Coupled Oscillators that Learn to Synchronizecited by 6
- 2022Blind Extraction of Equitable Partitions from Graph Signalscited by 0
- 2023On the convergence of nonlinear averaging dynamics with three-body interactions on hypergraphscited by 0
- 2024Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs5 Jun 2024
- 2023On the convergence of nonlinear averaging dynamics with three-body interactions on hypergraphs14 Apr 2023
- 2022Blind Extraction of Equitable Partitions from Graph Signals10 Mar 2022
- 2021KuraNet: Systems of Coupled Oscillators that Learn to Synchronize6 May 2021
- 2021A nonlinear diffusion method for semi-supervised learning on hypergraphs27 Mar 2021