Generalized Laplacian Regularized Framelet Graph Neural Networks

• URL: http://arxiv.org/abs/2210.15092v2
• Date: Thu, 13 Jul 2023 10:30:34 GMT
• Title: Generalized Laplacian Regularized Framelet Graph Neural Networks
• Authors: Zhiqi Shao, Andi Han, Dai Shi, Andrey Vasnev and Junbin Gao
• Abstract summary: 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.
• Score: 25.365685662593652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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