Generalization Error of Graph Neural Networks in the Mean-field Regime

• URL: http://arxiv.org/abs/2402.07025v3
• Date: Mon, 1 Jul 2024 09:27:34 GMT
• Title: Generalization Error of Graph Neural Networks in the Mean-field Regime
• Authors: Gholamali Aminian, Yixuan He, Gesine Reinert, Łukasz Szpruch, Samuel N. Cohen,
• Abstract summary: We explore two widely utilized types of graph neural networks: graph convolutional neural networks and message passing graph neural networks. Our novel approach involves deriving upper bounds within the mean-field regime for evaluating the generalization error of these graph neural networks.
• Score: 10.35214360391282
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work provides a theoretical framework for assessing the generalization error of graph neural networks in the over-parameterized regime, where the number of parameters surpasses the quantity of data points. We explore two widely utilized types of graph neural networks: graph convolutional neural networks and message passing graph neural networks. Prior to this study, existing bounds on the generalization error in the over-parametrized regime were uninformative, limiting our understanding of over-parameterized network performance. Our novel approach involves deriving upper bounds within the mean-field regime for evaluating the generalization error of these graph neural networks. We establish upper bounds with a convergence rate of $O(1/n)$, where $n$ is the number of graph samples. These upper bounds offer a theoretical assurance of the networks' performance on unseen data in the challenging over-parameterized regime and overall contribute to our understanding of their performance.

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