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Mar, 2024
图神经聚合-扩散与亚稳态
Graph Neural Aggregation-diffusion with Metastability
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Kaiyuan Cui, Xinyan Wang, Zicheng Zhang, Weichen Zhao
TL;DR
基于微分方程的连续图神经模型拓展了图神经网络的架构,通过聚合-扩散方程启发的GRADE模型在非线性扩散和聚合之间找到了一种微妙的平衡,通过产生亚稳态节点表示聚集成多个聚类,从而缓解了过度平滑的问题,该模型达到了竞争性的性能,证明了其在图神经网络中减轻过度平滑问题的作用。
Abstract
continuous graph neural models
based on differential equations have expanded the architecture of graph neural networks (GNNs). Due to the connection between graph diffusion and message passing,
diffusion-based models
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