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Generalized Laplacian Regularized Framelet Graph Neural Networks

arXiv 2210.15092 ↗First posted 27 Oct 2022cs.LGcited by 0

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Abstract (arXiv)

This paper introduces a novel Framelet Graph approach based on p-Laplacian GNN. The proposed two models, named p-Laplacian undecimated framelet graph convolution (pL-UFG) and generalized p-Laplacian undecimated framelet graph convolution (pL-fUFG) inherit the nature of p-Laplacian with the expressive power of multi-resolution decomposition of graph signals. The empirical study highlights the excellent performance of the pL-UFG and pL-fUFG in different graph learning tasks including node classification and signal denoising.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 41 of its 69 references that are in the map. First posted on arXiv 27 Oct 2022.

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References · 41

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29 more
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