Gem

This repo covers the implementation for our paper Gem.

Wanyu Lin, Hao Lan, and Baochun Li. "Generative Causal Explanations for Graph Neural Networks," in the Proceedings of the 38th International Conference on Machine Learning (ICML 2021), Online, July 18-24, 2021.

Download code

git clone https://github.com/wanyu-lin/ICML2021-Gem Gem
cd Gem
git submodule init
git submodule update
cd gnnexp
git apply --ignore-space-change --ignore-whitespace ../gnnexp.patch

Setup environment

Create an environment with conda:

conda create -n gem python=3.8.8
conda activate gem

Install PyTorch with CUDA 10.2:

conda install pytorch cudatoolkit=10.2 -c pytorch

Or install PyTorch without CUDA:

conda install pytorch cpuonly -c pytorch

Install other required packages:

conda install opencv scikit-learn networkx pandas matplotlib seaborn
pip install tensorboardx

Prepare classification models

See preparation.md or extract from zip file:

unzip ckpt.zip
unzip gnnexp_explanation.zip

Distillation

python generate_ground_truth.py --dataset=syn1 --top_k=6
python generate_ground_truth.py --dataset=syn4 --top_k=6
python generate_ground_truth_graph_classification.py --dataset=Mutagenicity --output=mutag --graph-mode --top_k=20
python generate_ground_truth_graph_classification.py --dataset=NCI1 --output=nci1_dc --graph-mode --top_k=20 --disconnected

or you can directly extract from zip file

unzip distillation.zip

Train Gem

python explainer_gae.py --dataset=syn1 --distillation=syn1_top6 --output=syn1_top6
python explainer_gae.py --dataset=syn4 --distillation=syn4_top6 --output=syn4_top6
python explainer_gae_graph.py --distillation=mutag_top20 --output=mutag_top20 --dataset=Mutagenicity --gpu -b 128 --weighted --gae3 --loss=mse --early_stop --graph_labeling --train_on_positive_label --epochs=300 --lr=0.01
python explainer_gae_graph.py --distillation=nci1_dc_top20 --output=nci1_dc_top20 --dataset=NCI1 --gpu -b 128 --weighted --gae3 --loss=mse --early_stop --graph_labeling --train_on_positive_label --epochs=300 --lr=0.01

Evaluate Gem

python test_explained_adj.py --dataset=syn1 --distillation=syn1_top6 --exp_out=syn1_top6 --top_k=6
python test_explained_adj.py --dataset=syn4 --distillation=syn4_top6 --exp_out=syn4_top6 --top_k=6
python test_explained_adj_graph.py --graph-mode --dataset=Mutagenicity --exp_out=mutag_top20 --distillation=mutag_top20 --top_k=15 --test_out=mutag_top20_top15
python test_explained_adj_graph.py --graph-mode --dataset=NCI1 --exp_out=nci1_dc_top20 --distillation=nci1_dc_top20 --top_k=15 --test_out=nci1_dc_top20_top15

Visualization

Run *.ipynb files in Jupyter Notebook or Jupyter Lab.

Reference

If you make advantage of Gem in your research, please cite the following in your manuscript:

@inproceedings{
    wanyu-icml21,
    title="{Generative Causal Explanations for Graph Neural Networks}",
    author={Lin, Wanyu and Lan, Hao and Li, Baochun},
    booktitle={International Conference on Machine Learning},
    year={2021},
    url={https://arxiv.org/pdf/2104.06643.pdf},
}