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[The Web Conference 2024] GNNShap: Scalable and Accurate GNN Explanation using Shapley Values

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# GNNShap: Scalable and Accurate GNN Explanation using Shapley Values
This repository contains the source code of
`GNNShap: Scalable and Accurate GNN Explanation using Shapley Values` paper accepted in The
Web Conference 2024.

### Setup
Our implementation is based on PyTorch and PYG. Also, Our Shapley sampling strategy is implemented 
in Cuda. Therefore, GNNShap requires a GPU with Cuda support.

First install PyTorch with GPU support from [here](https://pytorch.org/get-started/locally/) and 
make sure PyTorch is using GPU.

The rest of the required packages and versions are provided in the `requirements.txt` file.

You can install the requirements by running:
```bash
pip install -r requirements.txt
```

### Dataset Configs

Dataset and dataset-specific model configurations are in the `dataset/configs.py` file.

### Model training

We provided pretrained models in the `pretrained` folder.

To train Cora, CiteSeer, PubMed, Coauthor-CS, Coauthor-Physics, and Facebook datasets: 
```bash
python train.py --dataset Cora
```

Reddit and ogbn-products require `NeighborLoader` for training. To train them:
```bash
python train_large.py --dataset Reddit
```

### Experiments

We provided scripts for baselines and GNNShap experiments. Scripts will save explanation results to
the results folder. Note that scripts repeat each experiment five times. This can be changed in the 
scripts.

For baselines, you can use the following script. For individual baseline, you can refer to 
the script file content.

```bash
./run_baseline_experiments.sh
```

For GNNShap experiments, you can use the following script:
```
./run_gnnshap_experiments.sh
```

- _We ran experiments on a GPU with 24GB of memory. You may need to adjust `batch_size` 
and `num_samples` parameters if you have less GPU memory._
- _The first run might take some time: it needs to compile the Cuda code._

For individual dataset experiments, an example is provided below:
```bash
python run_gnnshap.py --dataset Cora --num_samples 25000 --sampler GNNShapSampler 
--batch_size 1024 --repeat 1
```

The results will be saved to the `results` folder. The default result folder can be changed 
in `dataset/configs.py`

### Evaluation
We used the `BaselinesEvaluation.ipynb`  notebook under the `examples` folder for explanation times
and fidelity results.

### Visualization
We provided explanation visualization examples in the `Visualization.ipynb`  notebook 
under `examples.`

### Custom Model & Data Explanations
We provided an example in the `CustomModelData.ipynb`  notebook under `examples`.

### Citation
Please cite our work if you find it useful.

```
Selahattin Akkas and Ariful Azad. 2024. GNNShap: Scalable and Accurate GNN Explanation using Shapley Values.
In Proceedings of the ACM Web Conference 2024 (WWW ’24), May 13–17, 2024, Singapore, Singapore.
```

GNNShap

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