Hyperbolic Graph Convolutional Networks in PyTorch

This repository is a fork of https://github.com/HazyResearch/hgcn, and additions/modifications are made by Eli and Chris.

We use their implementation of Hyperbolic Graph Convolutions [1] in PyTorch to examine how embedding on different manifolds can impact performance on link prediction and also node classification.

See examples in this Colab! Also check out our final paper!

This is also a class project for CS468 at Stanford.

Most of the code was forked from the following repositories

• hgcn
• pygcn
• gae
• hyperbolic-image-embeddings
• pyGAT
• poincare-embeddings
• geoopt

References

[1] Chami, I., Ying, R., Ré, C. and Leskovec, J. Hyperbolic Graph Convolutional Neural Networks. NIPS 2019.

[2] Nickel, M. and Kiela, D. Poincaré embeddings for learning hierarchical representations. NIPS 2017.

[3] Ganea, O., Bécigneul, G. and Hofmann, T. Hyperbolic neural networks. NIPS 2017.

[4] Kipf, T.N. and Welling, M. Semi-supervised classification with graph convolutional networks. ICLR 2017.

[5] Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P. and Bengio, Y. Graph attention networks. ICLR 2018.