Explainable Spatio-Temporal Graph Neural Networks

This is the PyTorch implementation by @Jiabin Tang for STExplainer model proposed in this paper:

Explainable Spatio-Temporal Graph Neural Networks
Jiabin Tang, Lianghao Xia, Chao Huang*
CIKM 2023

* denotes corresponding author

In this paper, we propose an Explainable Spatio-Temporal Graph Neural Networks (STExplainer) framework that enhances STGNNs with inherent explainability, enabling them to provide accurate predictions and faithful explanations simultaneously. Our framework integrates a unified spatio-temporal graph attention network with a positional information fusion layer as the STG encoder and decoder, respectively. Furthermore, we propose a structure distillation approach based on the Graph Information Bottleneck (GIB) principle with an explainable objective, which is instantiated by the STG encoder and decoder.

Environment

Please first clone the repo and install the required environment, which can be done by running the following commands:

conda env create -n stexplainer python=3.8

conda activate stexplainer

# Torch with CUDA 11.6
pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu116
# Clone our STExplainer
git clone https://github.com/HKUDS/STExplainer.git
cd STExplainer
# Install required libraries
pip install -r requirements.txt

Dataset

We utilized three traffic datasets and two crime datasets to evaluate STExplainer: PEMS4, 7, 8 (Traffic), NYC, CHI crime (Crime).

Examples to run the codes

We could modify configuration at ./config to train or test our model on different datasets. There is an example on PEMS04:

• train PEMS4 (note that the "testonly" in configuration file should be 0)

python train.py --config ./config/STExplainer_pems4.yaml

• test PEMS4 (note that the "testonly" in configuration file should be 1, and there is a corresponding checkpoints at ./results/model)

python train.py --config ./config/STExplainer_pems4.yaml

Reference

If you find this work is helpful to your research, please consider citing our paper:

@inproceedings{10.1145/3583780.3614871,
author = {Tang, Jiabin and Xia, Lianghao and Huang, Chao},
title = {Explainable Spatio-Temporal Graph Neural Networks},
year = {2023},
isbn = {9798400701245},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3583780.3614871},
doi = {10.1145/3583780.3614871},
booktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
pages = {2432–2441},
numpages = {10},
location = {Birmingham, United Kingdom},
series = {CIKM '23}
}

Acknowledgements

The structure of our code is based on GSAT, BasicTS and GIB. Thank for their work.