Deep Graph Infomax (Veličković et al., ICLR 2019): https://arxiv.org/abs/1809.10341

Here we provide an implementation of Deep Graph Infomax (DGI) in PyTorch, along with a minimal execution example (on the Cora dataset). The repository is organised as follows:

• data/ contains the necessary dataset files for Cora;
• models/ contains the implementation of the DGI pipeline (dgi.py) and our logistic regressor (logreg.py);
• layers/ contains the implementation of a GCN layer (gcn.py), the averaging readout (readout.py), and the bilinear discriminator (discriminator.py);
• utils/ contains the necessary processing subroutines (process.py).

Finally, execute.py puts all of the above together and may be used to execute a full training run on Cora.

If you make advantage of DGI in your research, please cite the following in your manuscript:

@inproceedings{
velickovic2018deep,
title="{Deep Graph Infomax}",
author={Petar Veli{\v{c}}kovi{\'{c}} and William Fedus and William L. Hamilton and Pietro Li{\`{o}} and Yoshua Bengio and R Devon Hjelm},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=rklz9iAcKQ},
}

MIT