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In this paper, we study the robustness of graph convolutional networks (GCNs).
Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Learning with a Strong Adversary
Ruitong Huang, Bing Xu, Dale Schuurmans, and Csaba Szepesvari · 2015
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Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2016
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Adversarial Machine Learning at Scale
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
Earlier work this paper cites.
Adversarial Machine Learning at Scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David A. Wagner · 2017
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ZOO: zeroth order optimization based black-box attacks to deep neural networks without training substitute models
Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, and Cho-Jui Hsieh · 2017
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Inductive Representation Learning on Large Graphs
W. L. Hamilton, R. Ying, and J. Leskovec · 2017
Earlier work this paper cites.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Query-limited black-box attacks to classifiers
Fnu Suya, Yuan Tian, David Evans, and Paolo Papotti · 2017
Cited alongside, same era.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2017
Cited alongside, same era.
Efficient Defenses Against Adversarial Attacks
Valentina Zantedeschi, Maria-Irina Nicolae, and Ambrish Rawat · 2017
Cited alongside, same era.
Fastgcn: Fast learning with graph convolutional networks via importance sampling
Towards robust neural networks via random self-ensemble
Xuanqing Liu, Minhao Cheng, Huan Zhang, and Cho-Jui Hsieh · 2018
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Graph convolutional networks with argument-aware pooling for event detection, 2018
Thien Nguyen and Ralph Grishman · 2018
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Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
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Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec · 2018
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Adversarial Attacks on Neural Networks for Graph Data
D. Zügner, A. Akbarnejad, and S. Günnemann · 2018
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Jie Chen, Tengfei Ma, and Cao Xiao · 2018
Cited alongside, same era.
Query-efficient hard-label black-box attack: An optimization-based approach
Minhao Cheng, Thong Le, Pin-Yu Chen, Jinfeng Yi, Huan Zhang, and Cho-Jui Hsieh · 2018
Cited alongside, same era.
Adversarial attack on graph structured data
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song · 2018
Cited alongside, same era.
Black-box Adversarial Attacks with Limited Queries and Information
A. Ilyas, L. Engstrom, A. Athalye, and J. Lin · 2018
Cited alongside, same era.
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
Cited alongside, same era.
Shen Wang, Zhengzhang Chen, Jingchao Ni, Xiao Yu, Zhichun Li, Haifeng Chen, and Philip S. Yu · 2019
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Adversarial Examples on Graph Data: Deep Insights into Attack and Defense
H. Wu, C. Wang, Y. Tyshetskiy, A. Docherty, K. Lu, and L. 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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