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NetGAN: Generating Graphs via Random Walks

Implementation of the method proposed in the paper:
NetGAN: Generating Graphs via Random Walks

by Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann
Published at ICML 2018 in Stockholm, Sweden.

This implementation is written in Python 3.6 and uses Tensorflow 1.4.1.

Requirements

Install the reqirements via
pip install -r requirements.txt

Note that the modules powerlaw and python-igraph are only needed to compute the graph statistics. If you only want to run NetGAN, feel free to comment out the respective parts of the code.

Run the code

To try our code, the best way to do so is to use the IPython notebook demo.ipynb

Pre-trained models used in the paper

Run graph_generation_pretrained.ipynb and link_prediction_pretrained.ipynb to try our pre-trained models on Cora-ML.

Latent variable interpolation

Run latent_interpolation.ipynb to run latent variable interpolation experiments as in the paper.

Installation

To install the package, run python setup.py install.

Citation

Please cite our paper if you use the model or this code in your own work:

@inproceedings{DBLP:conf/icml/BojchevskiSZG18,
  author    = {Aleksandar Bojchevski and
               Oleksandr Shchur and
               Daniel Z{\"{u}}gner and
               Stephan G{\"{u}}nnemann},
  title     = {NetGAN: Generating Graphs via Random Walks},
  booktitle = {Proceedings of the 35th International Conference on Machine Learning,
               {ICML} 2018, Stockholmsm{\"{a}}ssan, Stockholm, Sweden, July
               10-15, 2018},
  pages     = {609--618},
  year      = {2018},
}

References

Cora dataset

In the data folder you can find the Cora-ML dataset. The raw data was originally published by

McCallum, Andrew Kachites, Nigam, Kamal, Rennie, Jason, and Seymore, Kristie. "Automating the construction of internet portals with machine learning." Information Retrieval, 3(2):127–163, 2000.

and the graph was extracted by

Bojchevski, Aleksandar, and Stephan Günnemann. "Deep gaussian embedding of attributed graphs: Unsupervised inductive learning via ranking." ICLR 2018.

Contact

Please contact zuegnerd@in.tum.de in case you have any questions.