PetarV- / Dgi
Licence: mit
Deep Graph Infomax (https://arxiv.org/abs/1809.10341)
Stars: β 326
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python
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DGI
Deep Graph Infomax (VeliΔkoviΔ et al., ICLR 2019): https://arxiv.org/abs/1809.10341
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
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