CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks

This repository is the official implementation of the paper CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks.

Requirements

To install requirements:

conda env create --file environment.yml

📋 This will create a conda environment called pytorch-geo

Training original models

To train the original GNN models for the BA-shapes dataset in the paper, cd into src and run this command:

python train.py --dataset=syn1

📋 For the Tree-Cycles dataset, the dataset argument should be "syn4". For the Tree-Grid dataset, it should be "syn5". All hyperparameter settings are listed in the defaults, and all models have the same hyperparameters.

Training CF-GNNExplainer

To train CF-GNNExplainer for each dataset, run the following commands:

python main_explain.py --dataset=syn1 --lr=0.01 --beta=0.5 --n_momentum=0.9 --optimizer=SGD
python main_explain.py --dataset=syn4 --lr=0.1 --beta=0.5 --optimizer=SGD
python main_explain.py --dataset=syn5 --lr=0.1 --beta=0.5 --optimizer=SGD

📋 This will create another folder in the main directory called 'results', where the results files will be stored.

Evaluation

To evaluate the CF examples, loop over all the files in the results folder by running the following command:

python evaluate.py

📋 This will print out the values for each metric, for each results file.

Pre-trained Models

The pretrained models are available in the models folder

Results

Our model achieves the following performance:

Model name	Dataset	Fidelity	Size	Sparsity	Accuracy
CF-GNNExplainer	Tree-Cycles	0.21	2.09	0.90	0.94
CF-GNNExplainer	Tree-Grid	0.07	1.47	0.94	0.96
CF-GNNExplainer	BA-Shapes	0.39	2.39	0.99	0.96