Reference implementation (example) of the model proposed in the paper:
Diffusion Improves Graph Learning
by Johannes Gasteiger, Stefan Weißenberger, Stephan Günnemann
Published at NeurIPS 2019.
This repository primarily contains a demonstration of enhancing a graph convolutional network (GCN) with graph diffusion convolution (GDC) in the notebook gdc_demo.ipynb.
The repository uses these packages:
pyyaml
tqdm>=4.36
numpy
scipy
seaborn
pytorch>=1.3
pytorch_geometric
GDC is also implemented as a transformation (preprocessing step) in PyTorch Geometric. So you can just apply it to your own dataset and see how your existing PyG model improves!
Please contact j.gasteiger@in.tum.de in case you have any questions.
Please cite our paper if you use the model or this code in your own work:
@inproceedings{gasteiger_diffusion_2019,
title = {Diffusion Improves Graph Learning},
author = {Gasteiger, Johannes and Wei{\ss}enberger, Stefan and G{\"u}nnemann, Stephan},
booktitle={Conference on Neural Information Processing Systems (NeurIPS)},
year = {2019}
}