/graph2gauss

Gaussian node embeddings. Implementation of "Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking".

Primary LanguagePythonMIT LicenseMIT

Graph2Gauss

Illustraion of 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.

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},
}