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Graph Wavelet Neural Network

ICLR 2019 · Graph learning › GNN architectures · approach: Theory of graph convolutional networks · 75 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace Fourier transform with graph wavelet transformSpectral CNN · Coraaccuracy: 73.3% -> 82.8%Helped
replace Fourier transform with graph wavelet transformSpectral CNN · Citeseeraccuracy: 58.9% -> 71.7%Helped
replace Fourier transform with graph wavelet transformSpectral CNN · Pubmedaccuracy: 73.9% -> 79.1%Helped
use graph wavelet convolution instead of polynomial spectral filteringChebyNet · Coraaccuracy: 81.2% -> 82.8%Helped
use graph wavelet convolution instead of polynomial spectral filteringChebyNet · Citeseeraccuracy: 69.8% -> 71.7%Helped
use graph wavelet convolution instead of polynomial spectral filteringChebyNet · Pubmedaccuracy: 74.4% -> 79.1%Helped
use graph wavelet convolution instead of GCN filteringGCN · Coraaccuracy: 81.5% -> 82.8%Helped
use graph wavelet convolution instead of GCN filteringGCN · Citeseeraccuracy: 70.3% -> 71.7%Helped
use graph wavelet convolution instead of GCN filteringGCN · Pubmedaccuracy: 79.0% -> 79.1%Helped
detach feature transformation from graph convolution (ablation)ChebyNet · Coraaccuracy: 81.2% -> 81.6%Helped
detach feature transformation from graph convolution (ablation)ChebyNet · Citeseeraccuracy: 69.8% -> 68.5%Hurt
detach feature transformation from graph convolution (ablation)ChebyNet · Pubmedaccuracy: 74.4% -> 78.6%Helped

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