An experimental investigation of kernels on graphs for collaborative recommendation and semisupervised classification

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Diffusion-Convolutional Neural Networks (2015, methodology): “Diffusion kernels provide an effective basis for node classification tasks [1].”
Simple Spectral Graph Convolution (2021, methodology): “We have proposed Simple Spectral Graph Convolution (S 2 GC), a method based on the Markov Diffusion Kernel (Section 3.2) whose feature maps emerge from the normalized Laplacian Regu-larization problem (Section 3.3).”
Diffusion Improves Graph Learning (2019, background): “[22] and more advanced random walks and operators that are not defined by powers of a transition matrix.”
Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification (2018, background): “A recent survey paper [10] compared several diffusion kernels on graphs.”