Equivariant Hypergraph Diffusion Neural Operators. (arXiv:2207.06680v1 [cs.LG])

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 …

arxiv diffusion hypergraph lg operators