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Power to the Relational Inductive Bias: Graph Neural Networks in Electrical Power Grids
[article]
2021
pre-print
The application of graph neural networks (GNNs) to the domain of electrical power grids has high potential impact on smart grid monitoring. Even though there is a natural correspondence of power flow to message-passing in GNNs, their performance on power grids is not well-understood. We argue that there is a gap between GNN research driven by benchmarks which contain graphs that differ from power grids in several important aspects. Additionally, inductive learning of GNNs across multiple power
doi:10.1145/3459637.3482464
arXiv:2109.03604v1
fatcat:owwrx5vhercwpizpno5fu6xc6i