graph convolution
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Analysis of Corrected Graph Convolutions
Machine learning for node classification on graphs is a prominent area driven by applications such as recommendation systems.
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Stability of Graph Scattering Transforms
Fernando Gama, Alejandro Ribeiro, Joan Bruna
Whenconsidering networkdata,regularconvolutionsdonot hold since the data domain presents an irregular structure given by the network topology.
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A.1 EquivalencebetweenPreConvSAandvanillaSA OurproposedPreConvSAisformulatedinEquation1. f0i =MLPs fli fl+1
Inspired by the depthwise separable convolution used in MobileNet [1], we extend this separable idea to graph convolution. We only perform MLPs on point features directly to learn thechannelcorrelation, andleverages theanisotropic reduction toaggregatethespatial correlation.
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