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Journal Article

Deciphering the imprint of topology on nonlinear dynamical network stability


Nitzbon,  Jan
Potsdam Institute for Climate Impact Research;


Schultz,  Paul
Potsdam Institute for Climate Impact Research;


Heitzig,  Jobst
Potsdam Institute for Climate Impact Research;


Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;


Hellmann,  Frank
Potsdam Institute for Climate Impact Research;

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Nitzbon, J., Schultz, P., Heitzig, J., Kurths, J., Hellmann, F. (2017): Deciphering the imprint of topology on nonlinear dynamical network stability. - New Journal of Physics, 19, 33029.

Cite as: https://publications.pik-potsdam.de/pubman/item/item_21706
Coupled oscillator networks show complex interrelations between topological characteristics of the network and the nonlinear stability of single nodes with respect to large but realistic perturbations. We extend previous results on these relations by incorporating sampling-based measures of the transient behaviour of the system, its survivability, as well as its asymptotic behaviour, its basin stability. By combining basin stability and survivability we uncover novel, previously unknown asymptotic states with solitary, desynchronized oscillators which are rotating with a frequency different from their natural one. They occur almost exclusively after perturbations at nodes with specific topological properties. More generally we confirm and significantly refine the results on the distinguished role tree-shaped appendices play for nonlinear stability. We find a topological classification scheme for nodes located in such appendices, that exactly separates them according to their stability properties, thus establishing a strong link between topology and dynamics. Hence, the results can be used for the identification of vulnerable nodes in power grids or other coupled oscillator networks. From this classification we can derive general design principles for resilient power grids. We find that striving for homogeneous network topologies facilitates a better performance in terms of nonlinear dynamical network stability. While the employed second-order Kuramoto-like model is parametrised to be representative for power grids, we expect these insights to transfer to other critical infrastructure systems or complex network dynamics appearing in various other fields.