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Graph Neural Networks (GNNs) have achieved promising results in tasks such as node classification and graph classification.
Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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An overview of microsoft academic service (mas) and applications
Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June Hsu, and Kuansan Wang · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio, et al · 2017
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Detecting backdoor attacks on deep neural networks by activation clustering
Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, and Biplav Srivastava · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Graph neural networks for social recommendation
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin · 2019
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Badnets: Evaluating backdooring attacks on deep neural networks
Tianyu Gu, Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg · 2019
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Molecular geometry prediction using a deep generative graph neural network
Elman Mansimov, Omar Mahmood, Seokho Kang, and Kyunghyun Cho · 2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao · 2019
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Graphsaint: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna · 2019
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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
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Universal litmus patterns: Revealing backdoor attacks in cnns
Soheil Kolouri, Aniruddha Saha, Hamed Pirsiavash, and Heiko Hoffmann · 2020
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Weight poisoning attacks on pre-trained models
Keita Kurita, Paul Michel, and Graham Neubig · 2020
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Reflection backdoor: A natural backdoor attack on deep neural networks
Yunfei Liu, Xingjun Ma, James Bailey, and Feng Lu · 2020
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Geom-gcn: Geometric graph convolutional networks
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang · 2020
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Crfl: Certifiably robust federated learning against backdoor attacks
Chulin Xie, Minghao Chen, Pin-Yu Chen, and Bo Li · 2021
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Backdoor attacks to graph neural networks
Zaixi Zhang, Jinyuan Jia, Binghui Wang, and Neil Zhenqiang Gong · 2021
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Backdoor learning: A survey
Yiming Li, Yong Jiang, Zhifeng Li, and Shu-Tao Xia · 2022
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Federated social recommendation with graph neural network
Zhiwei Liu, Liangwei Yang, Ziwei Fan, Hao Peng, and Philip S Yu · 2022
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Robust graph convolutional networks against adversarial attacks
Dingyuan Zhu, Ziwei Zhang, Peng Cui, and Wenwu Zhu · 2022
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Unnoticeable backdoor attacks on graph neural networks
Enyan Dai, Minhua Lin, Xiang Zhang, and Suhang Wang · 2023
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An embarrassingly simple approach for trojan attack in deep neural networks
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On certifying robustness against backdoor attacks via randomized smoothing
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Gnnguard: Defending graph neural networks against adversarial attacks
Xiang Zhang and Marinka Zitnik · 2020
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Time and space complexity of graph convolutional networks
Derrick Blakely, Jack Lanchantin, and Yanjun Qi · 2021
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Proflip: Targeted trojan attack with progressive bit flips
Huili Chen, Cheng Fu, Jishen Zhao, and Farinaz Koushanfar · 2021
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Backdoor attack with imperceptible input and latent modification
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Certified robustness of graph neural networks against adversarial structural perturbation
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An embarrassingly simple backdoor attack on self-supervised learning
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Efficient backdoor attacks for deep neural networks in real-world scenarios
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Rab: Provable robustness against backdoor attacks
Maurice Weber, Xiaojun Xu, Bojan Karlaš, Ce Zhang, and Bo Li · 2023
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A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability
Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, and Suhang Wang · 2024
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Trojan prompt attacks on graph neural networks
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A clean-label graph backdoor attack method in node classification task
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Rethinking graph backdoor attacks: A distribution-preserving perspective
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