Critical parameters of the synchronisation's stability for coupled maps in regular graphs
Juan Gancio (1), Nicol\'as Rubido (2,1) (1 - Universidad de la, Rep\'ublica, Instituto de F\'isica de Facultad de Ciencias, Montevideo 11400,, Uruguay 2 - University of Aberdeen, King's College, Institute for Complex, Systems, Mathematical Biology, Aberdeen AB24 3UE

TL;DR
This paper derives critical parameters for the stability of synchronization in coupled chaotic maps on regular and cyclic graphs, providing explicit formulas based on Laplacian eigenvalues and classifying graphs by topology.
Contribution
It introduces closed-form expressions for eigenvalues of regular graphs and extends stability analysis to cyclic graphs like k-cycles and k-M"obius ladders, highlighting topological effects.
Findings
Derived critical coupling strength, Lyapunov exponent, and link density for synchronization stability.
Provided explicit eigenvalue formulas for regular graphs and classified graphs by topology.
Compared stability differences between finite and infinite size graphs.
Abstract
Coupled Map Lattice (CML) models are particularly suitable to study spatially extended behaviours, such as wave-like patterns, spatio-temporal chaos, and synchronisation. Complete synchronisation in CMLs emerges when all maps have their state variables with equal magnitude, forming a spatially-uniform pattern that evolves in time. Here, we derive critical values for the parameters -- coupling strength, maximum Lyapunov exponent, and link density -- that control the synchronisation-manifold's linear stability of diffusively-coupled, identical, chaotic maps in generic regular graphs (i.e., graphs with uniform node degrees) and class-specific cyclic graphs (i.e., periodic lattices with cyclical node permutation symmetries). Our derivations are based on the Laplacian matrix eigenvalues, where we give closed-form expressions for the smallest non-zero eigenvalue and largest eigenvalue of…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNeural Networks Stability and Synchronization · Chaos control and synchronization · Nonlinear Dynamics and Pattern Formation
