We present graph wavelet neural network (GWNN), a novel graph convolutional
neural network (CNN), leveraging graph wavelet transform to address the
shortcomings of previous spectral graph CNN methods that depend on graph
Fourier transform. Different from graph Fourier transform, graph wavelet
transform can be obtained via a fast algorithm without requiring matrix
eigendecomposition with high computational cost. Moreover, graph wavelets are
sparse and localized in vertex domain, offering high efficiency and good
interpretability for graph convolution. The proposed GWNN significantly
outperforms previous spectral graph CNNs in the task of graph-based
semi-supervised classification on three benchmark datasets: Cora, Citeseer and
Pubmed.

我们提出了一种图波形神经网络（GWNN），通过使用图小波变换来解决以往依赖于图傅里叶变换的图谱卷积神经网络（CNN）方法的缺点，这些方法需要进行高计算成本的矩阵特征分解。与图傅里叶变换不同的是，图小波变换可以通过快速算法获得，无需矩阵特征分解。此外，图小波在顶点域中稀疏且局部化，可以提供高效性和良好的图卷积可解释性。经过实验，GWNN 在三个基准数据集上均明显优于以往的图谱 CNNs，在图半监督分类任务上具有更好的性能。