Code for paper CF^2: "Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning (WWW 2022)"

Overall

Pytorch implementation for paper "Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning".

To run the experiments, dgl-cuda library is required. https://docs.dgl.ai/install/index.html.
After installing dgl-cuda library, Please replace the graphconv.py file in the dgl library with the file (with the same name) provided in the gnn_cff root folder. This is for the relaxation purpose as described in the paper.
The training and explaining are independent phases. For you convenience, we provide pre-trained GNN model params. Under the project root folder, run:
```
unzip log.zip
```
To set the python path, under the project root folder, run:
```
source setup.sh
```
As an example, to generate explanations for node classification task (Tree-Cycles dataset as example), run:
```
python scripts/exp_node_tree_cycles.py
```
For generating explanations for graph classification task (Mutag0 dataset as example), run:
```
python scripts/exp_graph.py
```
The codes for training GNNs are also provided. For instance, for training GNN for node classification, run:
```
python scripts/train_node_classification.py
```
for graph classification, run:
```
python scripts/train_graph_classification.py
```