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A cursory reading of the literature suggests that we have made a lot of progress in designing effective adversarial defenses for Graph Neural Networks (GNNs).
CiteSeer: An automatic citation indexing system
C. Lee Giles, Kurt D. Bollacker, and Steve Lawrence · 1998
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
Aleksandar Bojchevski and Stephan Günnemann · 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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Batch virtual adversarial training for graph convolutional networks
Zhijie Deng, Yinpeng Dong, and Jun Zhu · 2019
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Latent adversarial training of graph convolution networks
Hongwei Jin and Xinhua Zhang · 2019
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Virtual adversarial training on graph convolutional networks in node classification
Ke Sun, Zhouchen Lin, Hantao Guo, and Zhanxing Zhu · 2019
Earlier work this paper cites.
Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2019
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Adversarial examples for graph data: Deep insights into attack and defense
Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, and Liming Zhu · 2019
Earlier work this paper cites.
Topology attack and defense for graph neural networks: An optimization perspective
Kaidi Xu, Hongge Chen, Sijia Liu, Pin Yu Chen, Tsui Wei Weng, Mingyi Hong, and Xue Lin · 2019
Earlier work this paper cites.
Comparing and detecting adversarial attacks for graph deep learning
Yingxue Zhang, Sakif Hossain Khan, and Mark Coates · 2019
Earlier work this paper cites.
Robust graph convolutional networks against adversarial attacks
Dingyuan Zhu, Peng Cui, Ziwei Zhang, and Wenwu Zhu · 2019
Earlier work this paper cites.
Adversarial attacks on graph neural networks via meta learning
Daniel Zügner and Stephan Günnemann · 2019
Earlier work this paper cites.
Smoothing adversarial training for GNN
J. Chen, X. Lin, H. Xiong, Y. Wu, H. Zheng, and Q. Xuan · 2020
Earlier work this paper cites.
AANE: Anomaly aware network embedding for anomalous link detection
Dongsheng Duan, Lingling Tong, Yangxi Li, Jie Lu, Lei Shi, and Cheng Zhang · 2020
Earlier work this paper cites.
Variational inference for graph convolutional networks in the absence of graph data and adversarial settings
Pantelis Elinas, Edwin V. Bonilla, and Louis Tiao · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Reliable graph neural networks via robust aggregation
Simon Geisler, Daniel Zügner, and Stephan Günnemann · 2020
Cited alongside, same era.
Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
Cited alongside, same era.
Edge dithering for robust adaptive graph convolutional networks
Vassilis N. Ioannidis and Georgios B. Giannakis · 2020
Cited alongside, same era.
Tensor graph convolutional networks for multi-relational and robust learning
Robustness of graph neural networks at scale
Simon Geisler, Tobias Schmidt, Hakan Sirin, Daniel Zügner, Aleksandar Bojchevski, and Stephan Günnemann · 2021
Later among the works it cites.
Graph neural networks: Adversarial robustness
Stephan Günnemann · 2021
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Robust graph convolutional networks with directional graph adversarial training
Weibo Hu, Chuan Chen, Yaomin Chang, Zibin Zheng, and Yunfei Du · 2021
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Unveiling anomalous nodes via random sampling and consensus on graphs
Vassilis N. Ioannidis, Dimitris Berberidis, and Georgios B. Giannakis · 2021
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Robust training of graph convolutional networks via latent perturbation
Hongwei Jin and Xinhua Zhang · 2021
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Adversarial attack on large scale graph
Jintang Li, Tao Xie, Chen Liang, Fenfang Xie, Xiangnan He, and Zibin Zheng · 2021
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Vassilis N. Ioannidis, Antonio G. Marques, and Georgios B. Giannakis · 2020
Cited alongside, same era.
Graph structure learning for robust graph neural networks
Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang · 2020
Cited alongside, same era.
Deeprobust: A pytorch library for adversarial attacks and defenses
Yaxin Li, Wei Jin, Han Xu, and Jiliang Tang · 2020
Cited alongside, same era.
Transferring robustness for graph neural network against poisoning attacks
Xianfeng Tang, Yandong Li, Yiwei Sun, Huaxiu Yao, Prasenjit Mitra, and Suhang Wang · 2020
Cited alongside, same era.
On adaptive attacks to adversarial example defenses
Florian Tramer, Nicholas Carlini, Wieland Brendel, and Aleksander Madry · 2020
Cited alongside, same era.
Graph information bottleneck
Tailin Wu, Hongyu Ren, Pan Li, and Jure Leskovec · 2020
Cited alongside, same era.
Towards an efficient and general framework of robust training for graph neural networks
Kaidi Xu, Sijia Liu, Pin-Yu Chen, Mengshu Sun, Caiwen Ding, Bhavya Kailkhura, and Xue Lin · 2020
Cited alongside, same era.
Later among the works it cites.
Learning to drop: Robust graph neural network via topological denoising
Dongsheng Luo, Wei Cheng, Wenchao Yu, Bo Zong, Jingchao Ni, Haifeng Chen, and Xiang Zhang · 2021
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Uncertainty-matching graph neural networks to defend against poisoning attacks
Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, and Andreas Spanias · 2021
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Adversarial immunization for certifiable robustness on graphs
Shuchang Tao, H. Shen, Q. Cao, L. Hou, and Xueqi Cheng · 2021
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A lightweight metric defence strategy for graph neural networks against poisoning attacks
Yang Xiao, Jie Li, and Wengui Su · 2021
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Speedup robust graph structure learning with low-rank information
Hui Xu, Liyao Xiang, Jiahao Yu, Anqi Cao, and Xinbing Wang · 2021
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Detection and defense of topological adversarial attacks on graphs
Yingxue Zhang, Florence Regol, Soumyasundar Pal, Sakif Khan, Liheng Ma, and Mark Coates · 2021
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Graph robustness benchmark: Benchmarking the adversarial robustness of graph machine learning
Qinkai Zheng, Xu Zou, Yuxiao Dong, Yukuo Cen, Da Yin, Jiarong Xu, Yang Yang, and Jie Tang · 2021
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Generalization of neural combinatorial solvers through the lens of adversarial robustness
Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, and Stephan Günnemann · 2022
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Node copying: A random graph model for effective graph sampling
Florence Regol, Soumyasundar Pal, Jianing Sun, Yingxue Zhang, Yanhui Geng, and Mark Coates · 2022
Later among the works it cites.
Unsupervised adversarially-robust representation learning on graphs
Jiarong Xu, Yang Yang, Junru Chen, Chunping Wang, Xin Jiang, Jiangang Lu, and Yizhou Sun · 2022
Later among the works it cites.
Graph alternate learning for robust graph neural networks in node classification
Baoliang Zhang, Xiaoxin Guo, Zhenchuan Tu, and Jia Zhang · 2022
Later among the works it cites.