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.
@article{arxiv.1904.07785,
title = {Graph Wavelet Neural Network},
author = {Bingbing Xu and Huawei Shen and Qi Cao and Yunqi Qiu and Xueqi Cheng},
journal= {arXiv preprint arXiv:1904.07785},
year = {2019}
}