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We study the approximation power of Graph Neural Networks (GNNs) on latent position random graphs.
The Reduction of a Graph to Canonical Form and the Algebra Which Appears Therein
B Yu Weisfeiler and A A Leman · 1968
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Neural Message Passing for Quantum Chemistry
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Convergence and Stability of Graph Convolutional Networks on Large Random Graphs
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On the equivalence between graph isomorphism testing and function approximation with GNNs
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Universal Invariant and Equivariant Graph Neural Networks
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Ron Levie, Michael M. Bronstein, and Gitta Kutyniok · 2019
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The Power of Graph Convolutional Networks to Distinguish Random Graph Models
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Provably Powerful Graph Networks
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Building powerful and equivariant graph neural networks with message-passing
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A Comprehensive Survey on Graph Neural Networks
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On Size Generalization in Graph Neural Networks
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A functional perspective on learning symmetric functions with neural networks
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Characterizing the expressive power of invariant and equivariant graph neural networks
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