1 code implementation • 3 Oct 2023 • Christian Koke, Daniel Cremers
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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no code implementations • 30 Sep 2023 • Christian Koke, Abhishek Saroha, Yuesong Shen, Marvin Eisenberger, Daniel Cremers
To remedy these shortcomings, we introduce ResolvNet, a flexible graph neural network based on the mathematical concept of resolvents.
no code implementations • 26 Jan 2023 • Christian Koke, Gitta Kutyniok
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.
no code implementations • 26 Jan 2023 • Christian Koke
Stability to node-level perturbations is related to an 'adequate (spectral) covering' property of the filters in each layer.