Graph Wavelet Neural Network

Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng

ICLR 2019

/iclr/2019/xu2019iclr-graph/

Abstract

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.

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Cite

Text

Xu et al. "Graph Wavelet Neural Network." International Conference on Learning Representations, 2019.

Markdown

[Xu et al. "Graph Wavelet Neural Network." International Conference on Learning Representations, 2019.](https://mlanthology.org/iclr/2019/xu2019iclr-graph/)

BibTeX

@inproceedings{xu2019iclr-graph,
  title     = {{Graph Wavelet Neural Network}},
  author    = {Xu, Bingbing and Shen, Huawei and Cao, Qi and Qiu, Yunqi and Cheng, Xueqi},
  booktitle = {International Conference on Learning Representations},
  year      = {2019},
  url       = {https://mlanthology.org/iclr/2019/xu2019iclr-graph/}
}