Equivariant Hypergraph Diffusion Neural Operators

Understand

Hypergraph neural networks (HNNs) using neural networks to encode hypergraphs provide a promising way to model higher-order relations in data and further solve relevant prediction tasks built upon such higher-order relations.

• However, higher-order relations in practice contain complex patterns and are often highly irregular.
• So, it is often challenging to design an HNN that suffices to express those relations while keeping computational efficiency.
• Inspired by hypergraph diffusion algorithms, this work proposes a new HNN architecture named ED-HNN, which provably represents any continuous equivariant hypergraph diffusion operators that can model a wide range of higher-order relations.