Awesome

PPRGo (original, TensorFlow 1)

This repository provides the reference implementation of PPRGo for a single machine in TensorFlow 1. You can find an implementation in PyTorch in another repository. PPRGo is a fast GNN able to scale to massive graphs in both single-machine and distributed setups. It was proposed in our paper

Scaling Graph Neural Networks with Approximate PageRank
by Aleksandar Bojchevski*, Johannes Gasteiger*, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann
Published at ACM SIGKDD 2020.

*Both authors contributed equally to this research. Note that the author's name has changed from Johannes Klicpera to Johannes Gasteiger.

Demonstration

To see for yourself how fast PPRGo runs even on a large dataset we've set up a Google Colab notebook, which trains and generates predictions for the Reddit dataset, as described in the paper.

Installation

You can install the repository using pip install -e .. However, installing the requirements like this will result in TensorFlow using CUDA 10.0, which contains a bug that affects PPRGo. We recommend importing the Anaconda environment saved in environment.yaml instead, which provides the correct TensorFlow and CUDA versions.

Run the code

This repository contains a demo notebook for running training and inference (demo.ipynb) and a script for running the model on a cluster with SEML (run_seml.py).

Contact

Please contact a.bojchevski@in.tum.de or j.gasteiger@in.tum.de if you have any questions.

Cite

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

@inproceedings{bojchevski2020pprgo,
  title={Scaling Graph Neural Networks with Approximate PageRank},
  author={Bojchevski, Aleksandar and Gasteiger, Johannes and Perozzi, Bryan and Kapoor, Amol and Blais, Martin and R{\'o}zemberczki, Benedek and Lukasik, Michal and G{\"u}nnemann, Stephan},
  booktitle = {Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  year={2020},
  publisher = {ACM},
  address = {New York, NY, USA},
}