ReFine: Multi-Grained Explainability for GNNs

We are trying hard to update the code, but it may take a while to complete due to our tight schedule recently. Thank you for your waiting!

Installation

Requirements

• CPU or NVIDIA GPU, Linux, Python 3.7
• PyTorch, various Python packages

Main Packages

1. Pytorch Geometric. Official Download.

# We use TORCH version 1.6.0
CUDA=cu101
TORCH=1.6.0 
pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-geometric

2. Visual Genome. Google Drive Download. This is used for preprocessing the VG-5 dataset and visualizing the generated explanations. Manually download it to the same directory as data/. (Yes, this package can be installed using pip or API, but we find it slow to use).

Datasets

1. The processed raw data for BA-3motif is available in the data/ folder.
2. Datasets MNIST, Mutagenicity will be automatically downloaded when training models.
3. We select and label 4444 graphs from https://visualgenome.org/ to construct the VG-5 dataset. The graphs are labeled with five classes: stadium, street, farm, surfing, forest. Each graph contains regions of the objects as the nodes, while edges indicate the relationships between object nodes.

Download the dataset from Google Drive. Arrange the dir as

data ---BA3
 |------VG
        |---raw

Please remember to cite Visual Genome (bibtex) if you use our VG-5 dataset.

Training GNNs

cd gnns/
python ba3motif_gnn.py --epoch 100 --num_unit 2 --batch_size 128

The trained GNNs will be saved in param/gnns.

Explaining the Predictions

code is coming soon

Evaluation & Visualization

code is coming soon

Citation

Please cite our paper if you find the repository useful.

@inproceedings{2021refine,
  title={Towards Multi-Grained Explainability for Graph Neural Networks },
  author={Wang, Xiang and Wu, Ying-Xin and Zhang, An and He, Xiangnan and Chua, Tat-Seng},
  booktitle={Proceedings of the 35th Conference on Neural Information Processing Systems},
  year={2021} 
}