A Unified View Between Tensor Hypergraph Neural Networks And Signal Denoising

• URL: http://arxiv.org/abs/2309.08385v1
• Date: Fri, 15 Sep 2023 13:19:31 GMT
• Title: A Unified View Between Tensor Hypergraph Neural Networks And Signal Denoising
• Authors: Fuli Wang, Karelia Pena-Pena, Wei Qian, Gonzalo R. Arce
• Abstract summary: We show that the tensor-hypergraph convolutional network (T-HGCN) has emerged as a powerful architecture for preserving higher-order interactions on hypergraphs. We further design a tensor-hypergraph iterative network (T-HGIN) based on the HyperGSD problem, which takes advantage of a multi-step updating scheme in every single layer.
• Score: 7.083679120873857
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hypergraph Neural networks (HyperGNNs) and hypergraph signal denoising (HyperGSD) are two fundamental topics in higher-order network modeling. Understanding the connection between these two domains is particularly useful for designing novel HyperGNNs from a HyperGSD perspective, and vice versa. In particular, the tensor-hypergraph convolutional network (T-HGCN) has emerged as a powerful architecture for preserving higher-order interactions on hypergraphs, and this work shows an equivalence relation between a HyperGSD problem and the T-HGCN. Inspired by this intriguing result, we further design a tensor-hypergraph iterative network (T-HGIN) based on the HyperGSD problem, which takes advantage of a multi-step updating scheme in every single layer. Numerical experiments are conducted to show the promising applications of the proposed T-HGIN approach.

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