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A unified, mode-wise stability framework for deep GNN propagation that provides a principled characterization of over-smoothing is developed and Stability-Shaped Deep Graph Learning (SDGL) is proposed to mitigate over-smoothing in deep GNNs.
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 wide range of timescales. Addressing these challenges traditionally relies on model-based methods that, while accurate, can be too slow fo...
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