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WaveGSSM is introduced, a second-order graph state-space model that maintains two coupled latent states at each node, one for the current pattern and one for its temporal rate of change, and achieves the best mean performance across the temporal-graph benchmarks and global weather forecasting.
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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