Generalization error of graph neural networks in the mean-field regime
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...
| Main Authors: | , , , , |
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| 格式: | Conference item |
| 语言: | English |
| 出版: |
Proceedings of Machine Learning Research
2024
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| 总结: | 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 overparametrized 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 overparameterized regime and overall contribute to
our understanding of their performance. |
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