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.
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 .. 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.
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 a.bojchevski@in.tum.de or j.gasteiger@in.tum.de 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 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},
}