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Found 4 papers, 0 papers with code
HoloNets: Spectral Convolutions do extend to Directed Graphs
Within the graph learning community, conventional wisdom dictates that spectral convolutional networks may only be deployed on undirected graphs: Only there could the existence of a well-defined graph Fourier transform be guaranteed, so that information may be translated between spatial- and spectral domains.
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Node Classification
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ResolvNet: A Graph Convolutional Network with multi-scale Consistency
To remedy these shortcomings, we introduce ResolvNet, a flexible graph neural network based on the mathematical concept of resolvents.
Graph Scattering beyond Wavelet Shackles
This work develops a flexible and mathematically sound framework for the design and analysis of graph scattering networks with variable branching ratios and generic functional calculus filters.
Limitless stability for Graph Convolutional Networks
Stability to node-level perturbations is related to an 'adequate (spectral) covering' property of the filters in each layer.
