Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations-Reference-Cited by-同舟云学术

Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations

Published:2020-07-06 Issue: Volume: Page:
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Container-title:Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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Author:

Zügner Daniel¹,Günnemann Stephan¹

Affiliation:

1. Technical University of Munich, Munich, Germany

Funder

Bundesministerium für Bildung und Forschung

Deutsche Forschungsgemeinschaft

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3394486.3403217

Reference19 articles.

1. Jointly Constrained Biconvex Programming

2. Aleksandar Bojchevski and Stephan Günnemann. 2019. Adversarial Attacks on Node Embeddings via Graph Poisoning. In ICML. Aleksandar Bojchevski and Stephan Günnemann. 2019. Adversarial Attacks on Node Embeddings via Graph Poisoning. In ICML.

3. Aleksandar Bojchevski and Stephan Günnemann. 2019. Certifiable Robustness to Graph Perturbations. In NeurIPS. 8317--8328. Aleksandar Bojchevski and Stephan Günnemann. 2019. Certifiable Robustness to Graph Perturbations. In NeurIPS. 8317--8328.

4. Aleksandar Bojchevski and Stephan Günnemann. 2020. Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs Images and More. In ICML. Aleksandar Bojchevski and Stephan Günnemann. 2020. Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs Images and More. In ICML.

5. Hanjun Dai Hui Li Tian Tian Xin Huang Lin Wang Jun Zhu and Le Song. 2018. Adversarial Attack on Graph Structured Data. In ICML. Hanjun Dai Hui Li Tian Tian Xin Huang Lin Wang Jun Zhu and Le Song. 2018. Adversarial Attack on Graph Structured Data. In ICML.

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