Wuyxin/ReFine

Official code of "Towards Multi-Grained Explainability for Graph Neural Networks" (NeurIPS 2021) + Pytorch Implementation of recent attribution methods for GNNs

ReFine: Multi-Grained Explainability for GNNs

This is the official code for Towards Multi-Grained Explainability for Graph Neural Networks (NeurIPS 2021). Besides, we provide highly modularized explainers for Graph Classification Tasks. Some of them are adapted from the image domain. Below is a summary:

Explainer	Paper
ReFine	Towards Multi-Grained Explainability for Graph Neural Networks
SA	Explainability Techniques for Graph Convolutional Networks.
Grad-CAM	Explainability Methods for Graph Convolutional Neural Networks.
DeepLIFT	Learning Important Features Through Propagating Activation Differences
Integrated Gradients	Axiomatic Attribution for Deep Networks
GNNExplainer	GNNExplainer: Generating Explanations for Graph Neural Networks
PGExapliner	Parameterized Explainer for Graph Neural Network
PGM-Exapliner	PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks
Screener	Causal Screening to Interpret Graph Neural Networks
CXPlain	Cxplain: Causal Explanations for Model Interpretation under Uncertainty

Installation

Requirements

• CPU or NVIDIA GPU, Linux, Python 3.7
• PyTorch >= 1.5.0, other packages

1. Pytorch Geometric. Official Download.

# We use TORCH version 1.6.0
CUDA=cu102
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==1.7.0

2. Visual Genome (optional). 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. (This package can be accessed by API, but we found it slow to use.) You can still run the other datasets without downloading it.

3. Other packages

pip install tqdm logging pathlib matplotlib argparse json pgmpy==0.1.11 
# For visualization (optional) 
conda install -c conda-forge rdkit

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 4443 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 also cite Visual Genome (bibtex) if you use this dataset.

Train GNNs

We provide the trained GNNs in param/gnns for reproducing the results in our paper. To retrain the GNNs, run

cd gnns/
bash run.sh

The trained GNNs will be saved in param/gnns.

Explaining the Predictions

1. For global training of PGExplainer and ReFine, run

cd train/
bash run.sh

2. Load datasets

from utils.dataset import get_datasets
from torch_geometric.data import DataLoader

name = 'ba3'
train_dataset, val_dataset, test_dataset = get_datasets(name=name)
test_loader = DataLoader(test_dataset, batch_size=1)

3. Instantiate the explainer

from explainers import *

device = torch.device("cuda")
gnn_path = f'param/gnns/{name}_net.pt'

refine = torch.load(f'param/refine/{name}.pt') # load pretrained
refine.remap_device(device)

4. Explain

for g in test_loadder:
  refine.explain_graph(g, fine_tune=True, 
                      ratio=0.4, lr=1e-4, epoch=20)

For baseline explainers, e.g.,

gnn_explainer = GNNExplainer(device, gnn_path)
gnn_explainer.explain_graph(g,
                           epochs=100, lr=1e-2)
                           
screener = Screener(device, gnn_path)
screener.explain_graph(g)                 

5. Evaluation & Visualization

Evaluation and visualization are made universal for every explainer. After explaining a single graph, the pair (graph, edge_imp:np.ndarray) is saved as explainer.last_result by default, which is then evaluated or visualized.

ratios = [0.1 *i for i in range(1,11)]
acc_auc = refine.evaluate_acc(ratios).mean()
racall =  refine.evaluate_recall(topk=5)
refine.visualize(vis_ratio=0.3) # visualize the explanation

To evaluate ReFine-FT and ReFine in the testing datasets, run

python evaluate.py --dataset ba3

The results will be included in file results/ba3_results.json, where ReFine-FT.ACC-AUC (ReFine-FT.Recall@5) and ReFine.ACC-AUC (ReFine.Recall@5) are the performances of ReFine-FT and ReFine, respectively.

Citation

Please cite our paper if you find the repository useful.

@inproceedings{refine,
  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} 
}