  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.
