Official implementation of SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks. Personal websites of the main authors: Zhenhua Huang, Kunhao Li
- torch==2.0.0
- torch_geometric==2.3.0
- The real-world datasets include Cora, CiteSeer, and PolBlogs.
- The synthetic datasets include BA-Shape, BA-Community, Tree-Cycle, and Tree-Grid. Specifically, the download URL of datasets can refer to https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html
python main.py
- Cite as follows:
@INPROCEEDINGS{10597945,
author={Huang, Zhenhua and Li, Kunhao and Wang, Shaojie and Jia, Zhaohong and Zhu, Wentao and Mehrotra, Sharad},
booktitle={2024 IEEE 40th International Conference on Data Engineering (ICDE)},
title={SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks},
year={2024},
volume={},
number={},
pages={2945-2958},
keywords={Training;Bridges;Accuracy;Reliability engineering;Data engineering;Graph neural networks;Generators;Graph Neural Networks;Model Explanation;Node Classification;Self-Supervised Learning},
doi={10.1109/ICDE60146.2024.00229}}