Ying, Zhitao and Bourgeois, Dylan and You, Jiaxuan and Zitnik, Marinka and Leskovec, Jure. "GNNExplainer: Generating Explanations for Graph Neural Networks". Advances in Neural Information Processing Systems 32. 2019.
Link: https://arxiv.org/abs/1903.03894
This tutorial is based on the example provided by the official Pytorch-Geometric repository.
Link: https://github.com/rusty1s/pytorch_geometric
- numpy
- scipy
- matplotlib
- pytorch
- pytorch-geometric
Cora dataset from [1].
[1] Yang, Zhilin, William Cohen, and Ruslan Salakhudinov. "Revisiting semi-supervised learning with graph embeddings." International conference on machine learning. 2016.
Please contact Juneyong Yang(laoconeth@kaist.ac.kr) or raise an issue in this repo.
This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2017-0-01779, A machine learning and statistical inference framework for explainable artificial intelligence)
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Project Name : A machine learning and statistical inference framework for explainable artificial intelligence (의사결정 이유를 설명할 수 있는 인간 수준의 학습·추론 프레임워크 개발)
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Managed by Ministry of Science and ICT/XAIC
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Participated Affiliation : UNIST, Korea Univ., Yonsei Univ., KAIST, AItrics
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Web Site : http://openXai.org
