1 citations · 1 across the 7 of their papers we have counts for
5 papers · 1 filter
Stability-Shaped Deep Graph Learning
Junyou Zhu, Langzhou He, Fenying Cai +7
In deep graph neural networks, increasing depth enlarges the receptive field but often leads to over-smoothing, where node representations tend to align. We develop a unified, mode…
Graph Machine Learning: An Opportunity for Power Systems
Martin Sadric, Sebastian Pütz, Christian Nauck +4
Modern power systems face growing operational complexity driven by the integration of renewable energy sources, decentralization, and the need for real-time decision-making across…
A graph theoretic view on small signal stability of inverter-based power grids
Iva Bačić, Jakob Niehues, Philipp C. Böttcher +5
Dynamic grid stability is traditionally ensured with synchronous generators. Modern grids rely substantially more on inverter-based resources, which require grid-forming control to…
Learning Dynamic Stability Landscapes in Synchronization Networks
Christian Nauck, Junyou Zhu, Michael Lindner +1
The robustness of synchronization is typically characterized by scalar, per-node stability indices whose dependence on topology is studied via network science or graph neural netwo…
A Unified Theory of Edge Weights: Stability of General Laplacian Networks from Matrix Phases and Asymmetry Rayleigh Ratios
Nina Kastendiek, Jakob Niehues, Frank Hellmann
We study the properties and stability of networks with arbitrary Laplacian coupling. Classic approaches to studying networked systems require unrealistic assumptions, including hom…