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Graph Neural Networks (GNNs) have achieved promising performance in various real-world applications.
Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2013 · 2013
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Robust graph convolutional networks against adversarial attacks. In KDD’19 . 1399–1407
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Open graph benchmark: Datasets for machine learning on graphs
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Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach. In WWW’20 . 673–683
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Adversarial attacks on neural networks for graph data. In KDD’18 . 2847–2856
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann. 2018 · 2018
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TextBugger: Generating Adversarial Text Against Real-world Applications. In 26th Annual Network and Distributed System Security Symposium
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Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019 · 2019
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Generative adversarial nets. In NeurIPS’14 . 2672–2680
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014a
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Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung-Yu Hsieh, and Vasant Honavar. 2020 · 2020
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Scalable Attack on Graph Data by Injecting Vicious Nodes
Jihong Wang, Minnan Luo, Fnu Suya, Jundong Li, Zijiang Yang, and Qinghua Zheng. 2020 · 2020
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KDD CUP 2020 ML Track 2 Adversarial Attacks and Defense on Academic Graph 1st Place Solution
Qinkai Zheng, Yixiao Fei, Yanhao Li, Qingmin Liu, Minhao Hu, and Qibo Sun. 2020 · 2020
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Adversarial Attacks and Defenses on Graphs
Wei Jin, Yaxing Li, Han Xu, Yiqi Wang, Shuiwang Ji, Charu Aggarwal, and Jiliang Tang. 2021 · 2021
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