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Jun, 2020
图神经网络中过度平滑问题的注记
A Note on Over-Smoothing for Graph Neural Networks
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Chen Cai, Yusu Wang
TL;DR
本研究分析图神经网络在层数增加时出现的过度平滑现象，通过使用增广归一化拉普拉斯矩阵的频谱确定权重矩阵的条件，来说明当嵌入的狄利克雷能量收敛于零时，图嵌入的区分能力会丧失。通过使用狄利克雷能量来衡量嵌入的表达能力，可以得到比已有研究更简单的证明，并可处理更多的非线性问题。
Abstract
graph neural networks
(GNNs) have achieved a lot of success on graph-structured data. However, it is observed that the performance of
graph neural networks
does not improve as the number of layers increases. This
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