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Graph Neural Networks (GNNs), neural network architectures targeted to learning representations of graphs, have become a popular learning model for prediction tasks on nodes, graphs and configurations of points, with wide success in practice.
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Equivariant Subgraph Aggregation Networks
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A PAC-bayesian approach to generalization bounds for graph neural networks
R. Liao, R. Urtasun, and R. Zemel · 2021
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L. Ruiz, F. Gama, and A. Ribeiro · 2021
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Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results
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How neural networks extrapolate: From feedforward to graph neural networks
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