MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation-Reference-Cited by-同舟云学术

MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentation

Published:2023-08-04 Issue: Volume: Page:
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Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Zhang Jiaxing¹^ORCID,Luo Dongsheng²^ORCID,Wei Hua³^ORCID

Affiliation:

1. New Jersey Institute of Technology, Newark, NJ, USA

2. Florida International University, Miami, FL, USA

3. NJIT & Arizona State University, Newark, NJ, USA

Funder

NSF (National Science Foundation)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599435

Reference59 articles.

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2. Federico Baldassarre and Hossein Azizpour. 2019. Explainability Techniques for Graph Convolutional Networks. Federico Baldassarre and Hossein Azizpour. 2019. Explainability Techniques for Graph Convolutional Networks.

3. Muhammed Fatih Balin , Abubakar Abid , and James Zou . 2019 . Concrete autoencoders: Differentiable feature selection and reconstruction . In International conference on machine learning. PMLR, 444--453 . Muhammed Fatih Balin, Abubakar Abid, and James Zou. 2019. Concrete autoencoders: Differentiable feature selection and reconstruction. In International conference on machine learning. PMLR, 444--453.

4. Joan Bruna , Wojciech Zaremba , Arthur Szlam , and Yann LeCun . 2013. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 ( 2013 ). Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2013. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013).

5. Chun-Hao Chang Elliot Creager Anna Goldenberg and David Duvenaud. 2018. Explaining Image Classifiers by Counterfactual Generation. https://doi.org/10.48550/ARXIV.1807.08024 10.48550/ARXIV.1807.08024

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