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Generalization bounds for graph convolutional neural networks via Rademacher complexity

This paper aims at studying the sample complexity of graph convolutional neural networks (GCNs), by providing tight upper bounds of Rademacher complexity for GCN models with a single hidden layer.

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Cite this as

Shaogao Lv (2024). Dataset: Generalization bounds for graph convolutional neural networks via Rademacher complexity. https://doi.org/10.57702/prahg1q0

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Additional Info

Field	Value
Created	December 16, 2024
Last update	December 16, 2024
Defined In	https://doi.org/10.48550/arXiv.2102.10234
Author	Shaogao Lv