HNHN: Hypergraph Networks With Hyperedge Neurons
- Yihe Dong ,
- Will Sawin ,
- Yoshua Bengio
Graph Representation Learning and Beyond Workshop at ICML 2020 |
Code available: https://github.com/twistedcubic/HNHN.
Hypergraphs provide a natural representation for many real world datasets. We propose a novel framework, HNHN, for hypergraph representation learning. HNHN is a hypergraph convolution network with nonlinear activation functions applied to both hypernodes and hyperedges, combined with a normalization scheme that can flexibly adjust the importance of high-cardinality hyperedges and high-degree vertices depending on the dataset. We demonstrate improved performance of HNHN in both classification accuracy and speed on real world datasets when compared to state of the art methods.