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Jun, 2024
图神经网络在统计泛化中的多样视角
A Manifold Perspective on the Statistical Generalization of Graph Neural Networks
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Zhiyang Wang, Juan Cervino, Alejandro Ribeiro
TL;DR
通过流形理论,分析在由流形样本构造的图上操作的图神经网络的统计泛化差距,研究了图神经网络在节点级和图级任务上的泛化差距。在训练图中节点数量增加时,泛化差距减小,从而保证图神经网络对流形上的未见点的泛化性能。通过多个真实世界数据集验证了我们的理论结果。
Abstract
convolutional neural networks
have been successfully extended to operate on graphs, giving rise to
graph neural networks
(GNNs). GNNs combine information from adjacent nodes by successive applications of graph co
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