This repository provides a PyTorch implementation of PPRGo for a single machine. You can find the original TensorFlow 1 implementation 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 Klicpera*, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann
Published at ACM SIGKDD 2020.

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.

You can install the repository using pip install -e .. Since CUDA 10.0 includes a bug that affects PPRGo we strongly recommend using e.g. 10.1.

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).

Please contact [email protected] or [email protected] if you have any questions.

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 Klicpera, 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},
}