https://github.com/petarv-/dgi

Deep Graph Infomax (https://arxiv.org/abs/1809.10341)
https://github.com/petarv-/dgi

deep-graph-infomax graph-convolutional-networks neural-networks python pytorch unsupervised-learning unsupervised-node-embedding

Last synced: 4 days ago
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Deep Graph Infomax (https://arxiv.org/abs/1809.10341)

• Host: GitHub
• URL: https://github.com/petarv-/dgi
• Owner: PetarV-
• License: mit
• Created: 2018-12-21T15:06:57.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2022-11-01T16:50:49.000Z (about 2 years ago)
• Last Synced: 2024-07-21T14:33:14.803Z (4 months ago)
• Topics: deep-graph-infomax, graph-convolutional-networks, neural-networks, python, pytorch, unsupervised-learning, unsupervised-node-embedding
• Language: Python
• Size: 136 KB
• Stars: 617
• Watchers: 12
• Forks: 138
• Open Issues: 11
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# DGI

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

![](https://camo.githubusercontent.com/f62a0b987d8a1a140a9f3ba14baf4caa45dfbcad/68747470733a2f2f7777772e64726f70626f782e636f6d2f732f757a783779677761637a76747031302f646565705f67726170685f696e666f6d61782e706e673f7261773d31)

## Overview

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.

## Reference

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},

}

```

## License

MIT