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Recent studies have shown that Graph Convolutional Networks (GCNs) are vulnerable to adversarial attacks on the graph structure.
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Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Adversarial attacks on neural networks for graph data
Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann · 2018
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A survey of adversarial learning on graph
Liang Chen, Jintang Li, Jiaying Peng, Tao Xie, Zengxu Cao, Kun Xu, Xiangnan He, and Zibin Zheng · 2020
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All you need is low (rank): Defending against adversarial attacks on graphs
Negin Entezari, Saba A. Al-Sayouri, Amirali Darvishzadeh, and Evangelos E. Papalexakis · 2020
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Reliable graph neural networks via robust aggregation
Simon Geisler, Daniel Zügner, and Stephan Günnemann · 2020
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Adversarial attacks and defenses on graphs: A review and empirical study
Wei Jin, Yaxin Li, Han Xu, Yiqi Wang, and Jiliang Tang · 2020
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Abstract interpretation based robustness certification for graph convolutional networks
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Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, and Xue Lin · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Robust graph convolutional networks against adversarial attacks
Dingyuan Zhu, Ziwei Zhang, Peng Cui, and Wenwu Zhu · 2019
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Adversarial attacks on graph neural networks via meta learning
Daniel Zügner and Stephan Günnemann · 2019
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Certifiable robustness and robust training for graph convolutional networks
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Invariance-preserving localized activation functions for graph neural networks
Luana Ruiz, Fernando Gama, Antonio Garcia Marques, and Alejandro Ribeiro · 2020
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Transferring robustness for graph neural network against poisoning attacks
Xianfeng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, and Suhang Wang · 2020
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Yiqing Xie, Sha Li, Carl Yang, Raymond Chi-Wing Wong, and Jiawei Han · 2020
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Node similarity preserving graph convolutional networks
Wei Jin, Tyler Derr, Yiqi Wang, Yao Ma, Zitao Liu, and Jiliang Tang · 2021
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