Instability in Complex Oscillator Networks: Limitations and Potentials of Network Measures and Machine Learning

Abstract:A central question of network science is how functional properties of systems emerge from their structure. For networked dynamical systems, structure is typically captured through network measures. We investigate the relationship between these measures and stability metrics across non-linear and linear oscillators, as well as real-world power grid topologies and dynamics. We find that this relationship is highly sensitive to the underlying ensemble: minor changes in the networks considered, such as going from mean degree 6 to mean degree 8, can invert the correlation between a network measure and stability. We also investigate network measures as inputs for machine learning, as well as Graph Neural Networks (GNNs) as predictors of stability. Both GNNs and the non-linear combination of many network measures can accurately predict stability within a given ensemble, yet both can fail when the ensemble changes. We conclude that neither approach reliably identifies the underlying structural causes of instability.

Adaptation and Self-Organizing Systems (nlin.AO); Machine Learning (cs.LG); Social and Information Networks (cs.SI)

arXiv:2402.17500 [nlin.AO]

(or arXiv:2402.17500v2 [nlin.AO] for this version)

https://doi.org/10.48550/arXiv.2402.17500 Focus to learn more arXiv-issued DOI via DataCite

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