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2021年1月12日 · Moreover, SSGC is comparable to other state-of-the-art methods for node clustering and community prediction tasks. One-sentence Summary : A simple and efficient method for graph convolution based on the Markov Diffusion Kernel, which works well on different tasks under unsupervised, semi-supervised and supervised settings.
Published as a conference paper at ICLR 2021 SIMPLE SPECTRAL GRAPH CONVOLUTION Hao Zhu, Piotr Koniusz Australian National University
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Spectral Graph Convolution (SSGC) (Zhu & Koniusz, 2021). Modern approaches incorporate both local and global features, such as L3Net (Cheng et al., 2020) and General, Powerful, Scalable (GPS) Graph Transformer (Rampášek et al., 2022). 2BACKGROUND
raining framework to control bias and train shallow modules correctly. (2) Under this framework, a multi-layer GNN can be decoupled into multiple simple GNNs, named as separable GNNs in this paper, so that every training step could use. the stochastic optimization without any samplings or changes on graph. Therefo.
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