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Graph neural networks, a popular class of models effective in a wide range of graph-based learning tasks, have been shown to be vulnerable to adversarial attacks.
Backpropagation applied to handwritten zip code recognition
Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel · 1989
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A new view of automatic relevance determination
David P Wipf, Srikantan S Nagarajan, J Platt, D Koller, and Y Singer · 2007
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Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan Van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
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Slic superpixels compared to state-of-the-art superpixel methods
Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk · 2012
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Discriminative embeddings of latent variable models for structured data
Hanjun Dai, Bo Dai, and Le Song · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David Wagner · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Bayesian Optimization for Black-Box Evasion of Machine Learning Systems
Luis Munoz-González · 2017
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Query-limited black-box attacks to classifiers
Fnu Suya, Yuan Tian, David Evans, and Paolo Papotti · 2017
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A comprehensive survey of graph embedding: Problems, techniques, and applications
Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang · 2018
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Adversarial attack on graph structured data
Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song · 2018
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Adversarial attack and defense on graph data: A survey
Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Philip S Yu, Lifang He, and Bo Li · 2018
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The spread of true and false news online
Soroush Vosoughi, Deb Roy, and Sinan Aral · 2018
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Hiding individuals and communities in a social network
Marcin Waniek, Tomasz P Michalak, Michael J Wooldridge, and Talal Rahwan · 2018
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Hierarchical graph representation learning with differentiable pooling
Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L Hamilton, and Jure Leskovec · 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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Adversarial attacks on node embeddings via graph poisoning
Aleksandar Bojchevski and Stephan Günnemann · 2019
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Graph u-nets
Hongyang Gao and Shuiwang Ji · 2019
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Understanding attention and generalization in graph neural networks
Boris Knyazev, Graham W. Taylor, and Mohamed R. Amer · 2019
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Attacking graph convolutional networks via rewiring
Yao Ma, Suhang Wang, Tyler Derr, Lingfei Wu, and Jiliang Tang · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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Black-box adversarial attacks with bayesian optimization
Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott, and J Zico Kolter · 2019
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Wasserstein weisfeiler-lehman graph kernels
Matteo Togninalli, Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, and Karsten Borgwardt · 2019
Towards more practical adversarial attacks on graph neural networks
Jiaqi Ma, Shuangrui Ding, and Qiaozhu Mei · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020
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Bayesopt adversarial attack
Binxin Ru, Adam Cobb, Arno Blaas, and Yarin Gal · 2020
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Bridging the gap between sample-based and one-shot neural architecture search with bonas
Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T Kwok, and Tong Zhang · 2020
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Deep graph library: A graph-centric, highly-performant package for graph neural networks
Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, et al · 2019
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Adversarial examples on graph data: Deep insights into attack and defense
Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, and Liming Zhu · 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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On the design of black-box adversarial examples by leveraging gradient-free optimization and operator splitting method
Pu Zhao, Sijia Liu, Pin-Yu Chen, Nghia Hoang, Kaidi Xu, Bhavya Kailkhura, and Xue Lin · 2019
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A restricted black-box adversarial framework towards attacking graph embedding models
Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu, and Junzhou Huang · 2020
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Adversarial detection on graph structured data
Jinyin Chen, Huiling Xu, Jinhuan Wang, Qi Xuan, and Xuhong Zhang · 2020
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Hebo: Heteroscedastic evolutionary bayesian optimisation
Alexander I Cowen-Rivers, Wenlong Lyu, Zhi Wang, Rasul Tutunov, Hao Jianye, Jun Wang, and Haitham Bou Ammar · 2020
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Haoteng Tang, Guixiang Ma, Yurong Chen, Lei Guo, Wei Wang, Bo Zeng, and Liang Zhan · 2020
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Evasion attacks to graph neural networks via influence function
Binghui Wang, Tianxiang Zhou, Minhua Lin, Pan Zhou, Ang Li, Meng Pang, Cai Fu, Hai Li, and Yiran Chen · 2020
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Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen · 2020
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Backdoor attacks to graph neural networks
Zaixi Zhang, Jinyuan Jia, Binghui Wang, and Neil Zhenqiang Gong · 2020
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Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2020
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Adversarial attacks on graph neural networks: Perturbations and their patterns
Daniel Zügner, Oliver Borchert, Amir Akbarnejad, and Stephan Günnemann · 2020
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Graphattacker: A general multi-task graphattack framework
Jinyin Chen, Dunjie Zhang, Zhaoyan Ming, and Kejie Huang · 2021
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Interpretable stability bounds for spectral graph filters
Henry Kenlay, Dorina Thanou, and Xiaowen Dong · 2021
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Interpretable neural architecture search via bayesian optimisation with weisfeiler-lehman kernels
Binxin Ru, Xingchen Wan, Xiaowen Dong, and Michael Osborne · 2021
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Think global and act local: Bayesian optimisation over high-dimensional categorical and mixed search spaces
Xingchen Wan, Vu Nguyen, Huong Ha, Binxin Ru, Cong Lu, and Michael A Osborne · 2021
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Explainability-based backdoor attacks against graph neural networks
Jing Xu, Stjepan Picek, et al · 2021
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Bo-dba: Query-efficient decision-based adversarial attacks via bayesian optimization
Zhuosheng Zhang and Shucheng Yu · 2021
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