arXiv Machine Learning
Jul 30
Universality and Approximation Rates of Graph Neural Networks with Random Features
arXiv:2607. 26699v1 Announce Type: new Abstract: We investigate message-passing graph neural networks with random node features.
Universality and Approximation Rates of Graph Neural Networks with Random Features
We investigate message-passing graph neural networks with random node features. Random node features are known to enhance the expressiveness of graph neural networks (GNNs) both theoretically and empirically.
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Learning to Execute Graph Algorithms Exactly with Graph Neural Networks
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arXiv Machine Learning
Jul 30
On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry
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arXiv AI
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Introduction to Graph Neural Networks for Machine Learning Engineers
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Distance-Preserving Embeddings in Inhomogeneous Random Graphs
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Jul 23
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arXiv Machine Learning
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arXiv AI
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