abojchevski / Graph2gauss
Licence: mit
Gaussian node embeddings. Implementation of "Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking".
Stars: ✭ 135
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python
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Graph2Gauss
Tensorflow implementation of the method proposed in the paper: "Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking", Aleksandar Bojchevski and Stephan Günnemann, ICLR 2018.
Installation
python setup.py install
Requirements
- tensorflow (>=1.4)
- sklearn (only for evaluation)
Demo
See the notebook example.ipynb for a simple demo.
Graphs without attributes
If you graph has no attribute information you can run the one-hot version of Graph2Gauss (G2G_oh) by setting X=I, where I is the identity matrix. Additionally, setting X=A+I, where A is the adjacency matrix often yields even better perfomance.
Misc
For an animation of Graph2Gauss learning to embed nodes as 2D Gaussians see: https://twitter.com/abojchevski/status/958278834025091072?s=19
Cite
Please cite our paper if you use this code in your own work:
@inproceedings{
bojchevski2018deep,
title={Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking},
author={Aleksandar Bojchevski and Stephan Günnemann},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=r1ZdKJ-0W},
}
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