A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy-Reference-Cited by-同舟云学术

A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy

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:

Dai Enyan¹^ORCID,Cui Limeng²^ORCID,Wang Zhengyang²^ORCID,Tang Xianfeng²^ORCID,Wang Yinghan²^ORCID,Cheng Monica²^ORCID,Yin Bing²^ORCID,Wang Suhang¹^ORCID

Affiliation:

1. Pennsylvania State University, State College, USA

2. Amazon, Palo Alto, USA

Funder

National Science Foundation

Army Research Office

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599248

Reference57 articles.

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2. Alexander A Alemi , Ian Fischer , Joshua V Dillon , and Kevin Murphy . 2016. Deep variational information bottleneck. arXiv preprint arXiv:1612.00410 ( 2016 ). Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy. 2016. Deep variational information bottleneck. arXiv preprint arXiv:1612.00410 (2016).

3. Molecular generative Graph Neural Networks for Drug Discovery

4. Kamalika Chaudhuri , Claire Monteleoni , and Anand D Sarwate . 2011 . Differentially private empirical risk minimization . JMLR , Vol. 12 , 3 (2011). Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate. 2011. Differentially private empirical risk minimization. JMLR, Vol. 12, 3 (2011).

5. Liang Chen , Jintang Li , Qibiao Peng , Yang Liu , Zibin Zheng , and Carl Yang . 2021. Understanding structural vulnerability in graph convolutional networks. arXiv preprint arXiv:2108.06280 ( 2021 ). Liang Chen, Jintang Li, Qibiao Peng, Yang Liu, Zibin Zheng, and Carl Yang. 2021. Understanding structural vulnerability in graph convolutional networks. arXiv preprint arXiv:2108.06280 (2021).

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