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arXiv · 2006.12278

HNHN: Hypergraph Networks with Hyperedge Neurons

Yihe Dong·Will Sawin·Yoshua Bengio
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Abstract

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.

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Yihe Dong, Will Sawin, Yoshua Bengio. 2020-06-22. HNHN: Hypergraph Networks with Hyperedge Neurons. https://arxiv.org/abs/2006.12278

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