wehub-resource-syncLearning Deep Generative Models of Graphs
This is an implementation of Learning Deep Generative Models of Graphs by Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, Peter Battaglia.
For molecule generation, see DGL-LifeSci.
Dependencies
- Python 3.5.2
- Pytorch 0.4.1
- Matplotlib 2.2.2
Usage
python3 main.py
Performance
90% accuracy for cycles compared with 84% accuracy reported in the original paper.
Speed
On AWS p3.2x instance (w/ V100), one epoch takes ~526s.
Acknowledgement
We would like to thank Yujia Li for providing details on the implementation.