This is a Tensorflow implementation of the PGExplainer:
Parameterized Explainer for Graph Neural Network
NeurIPS 2020
Towards Inductive and Efficient Explanations for Graph Neural Networks
TPAMI 2024
- Python 3.6.8
- tensorflow 2.0
- networkx
Now, PGExplainer is avilable at pytorch_geometric
Here are several re-implementations and reproduction reports from other groups. Thanks very much these researchers for re-implementing PGExplainer to make it more easy to use!
- [Re] Parameterized Explainer for Graph Neural Network
https://zenodo.org/record/4834242/files/article.pdf
Code:
https://github.com/LarsHoldijk/RE-ParameterizedExplainerForGraphNeuralNetworks
Note that in this report, they adopt different GCN models with our implementation.
- DIG
https://github.com/divelab/DIG/tree/main/dig/xgraph/PGExplainer
- Reproducing: Parameterized Explainer for Graph NeuralNetwork
https://openreview.net/forum?id=tt04glo-VrT
Code:
https://openreview.net/attachment?id=tt04glo-VrT&name=supplementary_material
https://github.com/flyingdoog/awesome-graph-explainability-papers
@article{luo2020parameterized,
title={Parameterized Explainer for Graph Neural Network},
author={Luo, Dongsheng and Cheng, Wei and Xu, Dongkuan and Yu, Wenchao and Zong, Bo and Chen, Haifeng and Zhang, Xiang},
journal={Advances in Neural Information Processing Systems},
volume={33},
year={2020}
}
@article{luo2024towards,
title={Towards Inductive and Efficient Explanations for Graph Neural Networks},
author={Luo, Dongsheng and Zhao, Tianxiang and Cheng, Wei and Xu, Dongkuan and Han, Feng and Yu, Wenchao and Liu, Xiao and Chen, Haifeng and Zhang, Xiang},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2024},
publisher={IEEE}
}