  Graph convolutional neural network provides good solutions for node
classification and other tasks with non-Euclidean data. There are several graph
convolutional models that attempt to develop deep networks but do not cause
serious over-smoothing at the same time. Considering that the wavelet transform
generally has a stronger ability to extract useful information than the Fourier
transform, we propose a new deep graph wavelet convolutional network (DeepGWC)
for semi-supervised node classification tasks. Based on the optimized static
filtering matrix parameters of vanilla graph wavelet neural networks and the
combination of Fourier bases and wavelet ones, DeepGWC is constructed together
with the reuse of residual connection and identity mappings in network
architectures. Extensive experiments on three benchmark datasets including
Cora, Citeseer, and Pubmed are conducted. The experimental results demonstrate
that our DeepGWC outperforms existing graph deep models with the help of
additional wavelet bases and achieves new state-of-the-art performances
eventually.
