CELL

This is the official repository of the ICML 2020 paper "NetGAN without GAN: From Random Walks to Low-Rank Approximations".

Installation

The latest code can be installed directly from GitHub with:

$ pip install git+https://github.com/hheidrich/CELL.git

The code can be installed in development mode with:

$ git clone https://github.com/hheidrich/CELL.git
$ cd CELL
$ pip install -e .

Where -e means "editable" mode.

Citation

@InProceedings{pmlr-v119-rendsburg20a, title = {{N}et{GAN} without {GAN}: From Random Walks to Low-Rank Approximations}, author = {Rendsburg, Luca and Heidrich, Holger and Luxburg, Ulrike Von}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {8073--8082}, year = {2020}, volume = {119}, series = {Proceedings of Machine Learning Research}, publisher = {PMLR} }

References

Cora dataset

Under data/CORA-ML.npz 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.

The files data/CORA-ML_train.npz and link_prediction.p contain the train-validation-test-split of data/CORA-ML.npz used in "NetGAN without GAN: From Random Walks to Low-Rank Approximations".