Graph Navier Stokes Networks 结合对流克服过平滑问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-26 04:00

研究人员推出了一种新颖的图神经网络（GNN）架构——Graph Navier Stokes Networks（GNSN），旨在克服过平滑问题。与传统的基于扩散的方法不同，GNSN通过定义消息传播的动态速度场，将对流纳入图结构。这种方法允许对流和扩散之间更具适应性的平衡，从而在不同同质性水平的数据集上获得更好的性能。在十二个真实世界数据集上的评估表明，GNSN在分类准确性方面持续优于现有的最先进基线，并能有效缓解过平滑问题。 AI

影响引入了一种新颖的GNN架构，提高了分类准确性并缓解了过平滑问题，可能推动图基深度学习的研究。

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Graph Navier Stokes Networks 结合对流克服过平滑问题

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arXiv cs.LG TIER_1 English(EN) · Zexing Zhao, Guangsi Shi, Yu Gong, Tianyu Wang, Shirui Pan, Hongye Cheng, Yuxiao Li · 2026-05-26 04:00

Graph Navier Stokes Networks

arXiv:2605.21247v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have emerged as a cornerstone of deep learning, with most existing methods rooted in graph signal processing and diffusion equations to model message passing. However, these approaches inherently suf…

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Graph Navier Stokes Networks

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