Multiple timescale dynamics of network adaptation with constraints

Adaptive network dynamical systems describe the co-evolution of dynamical quantities on the nodes and the dynamics of the network connections themselves. For dense networks of many nodes, the resulting dynamics are typically high-dimensional. Here, we consider adaptive dynamical systems subject to constraints on network adaptation: Asymptotically, the adaptive dynamics of network connections evolves on a low-dimensional subset of possible connectivity. Such dimension reduction may be intrinsic to the adaptation rule or arise from an additional dynamical mechanism acting on a timescale distinct from that of network adaptation. We illustrate how network adaptation with various constraints influences the dynamics of Kuramoto oscillator networks and elucidates the role of multiple timescales in shaping the dynamics. Our results shed light on why one may expect effective low-dimensional adaptation dynamics in generally high-dimensional adaptive network dynamical systems.

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Publication Details

Journal
Chaos An Interdisciplinary Journal of Nonlinear Science
Published
2025-10-01
DOI
https://doi.org/10.1063/5.0289706
Citations
1
Primary Topic
Nonlinear Dynamics and Pattern Formation
Type
article
Field-Weighted Citation Impact
1.49
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article

Multiple timescale dynamics of network adaptation with constraints

Erik A. Martens, Christian Bick
1 citations
Chaos An Interdisciplinary Journal of Nonlinear Science
Nonlinear Dynamics and Pattern Formation
1.49
article

Multiple timescale dynamics of network adaptation with constraints

Erik A. Martens, Christian Bick
article en
1 citations

Abstract

Adaptive network dynamical systems describe the co-evolution of dynamical quantities on the nodes and the dynamics of the network connections themselves. For dense networks of many nodes, the resulting dynamics are typically high-dimensional. Here, we consider adaptive dynamical systems subject to constraints on network adaptation: Asymptotically, the adaptive dynamics of network connections evolves on a low-dimensional subset of possible connectivity. Such dimension reduction may be intrinsic to the adaptation rule or arise from an additional dynamical mechanism acting on a timescale distinct from that of network adaptation. We illustrate how network adaptation with various constraints influences the dynamics of Kuramoto oscillator networks and elucidates the role of multiple timescales in shaping the dynamics. Our results shed light on why one may expect effective low-dimensional adaptation dynamics in generally high-dimensional adaptive network dynamical systems.

Chaos An Interdisciplinary Journal of Nonlinear ScienceVol. 35(10)
Roskilde University (DK), Statistics Sweden (SE), University of Exeter (GB), Mathematical Institute of the Slovak Academy of Sciences (SK), Technical University of Munich (DE), Vrije Universiteit Amsterdam (NL)
Openalex Percentile: Top 12%
Nonlinear Dynamics and Pattern Formation
1.49
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Multiple timescale dynamics of network adaptation with constraints — Erik A. Martens, Christian Bick · Chaos An Interdisciplinary Journal of Nonlinear Science (2025) | TGRS Research Map | TGRS