A Unified View Between Tensor Hypergraph Neural Networks And Signal Denoising | IEEE Conference Publication | IEEE Xplore

A Unified View Between Tensor Hypergraph Neural Networks And Signal Denoising


Abstract:

Hypergraph Neural networks (HyperGNNs) and hypergraph signal denoising (HyperGSD) are two fundamental topics in higher-order network modeling. Understanding the connectio...Show More

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.
Date of Conference: 04-08 September 2023
Date Added to IEEE Xplore: 01 November 2023
ISBN Information:

ISSN Information:

Conference Location: Helsinki, Finland

Funding Agency:

No metrics found for this document.

No metrics found for this document.
Contact IEEE to Subscribe

References

References is not available for this document.