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Recent studies have revealed that GNNs are highly susceptible to multiple adversarial attacks.
Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008 · 2008
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2016 · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio, et al · 2017
Earlier work this paper cites.
Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen. 2018 · 2018
Earlier work this paper cites.
Graph neural networks for social recommendation. In The world wide web conference . 417–426
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019 · 2019
Earlier work this paper cites.
Badnets: Evaluating backdooring attacks on deep neural networks
Tianyu Gu, Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg. 2019 · 2019
Earlier work this paper cites.
Molecular geometry prediction using a deep generative graph neural network
Elman Mansimov, Omar Mahmood, Seokho Kang, and Kyunghyun Cho. 2019 · 2019
Earlier work this paper cites.
Provably powerful graph networks
Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman. 2019 · 2019
Earlier work this paper cites.
Explainability methods for graph convolutional neural networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition . 10772–10781
Phillip E Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann. 2019 · 2019
Earlier work this paper cites.
Neural cleanse: Identifying and mitigating backdoor attacks in neural networks. In 2019 IEEE Symposium on Security and Privacy (SP) . IEEE, 707–723
Bolun Wang, Yuanshun Yao, Shawn Shan, Huiying Li, Bimal Viswanath, Haitao Zheng, and Ben Y Zhao. 2019 · 2019
Earlier work this paper cites.
Gnnexplainer: Generating explanations for graph neural networks
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019 · 2019
Earlier work this paper cites.
Graphsaint: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. 2019 · 2019
Earlier work this paper cites.
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 · 2020
Earlier work this paper cites.
Parameterized explainer for graph neural network
Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang. 2020 · 2020
Earlier work this paper cites.
Hidden trigger backdoor attacks. In Proceedings of the AAAI conference on artificial intelligence , Vol. 34. 11957–11965
Aniruddha Saha, Akshayvarun Subramanya, and Hamed Pirsiavash. 2020 · 2020
Earlier work this paper cites.
Xgnn: Towards model-level explanations of graph neural networks. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining . 430–438
Hao Yuan, Jiliang Tang, Xia Hu, and Shuiwang Ji. 2020 · 2020
Cited alongside, same era.
Backdoor Defense via Decoupling the Training Process. In International Conference on Learning Representations
Kunzhe Huang, Yiming Li, Baoyuan Wu, Zhan Qin, and Kui Ren. 2021 · 2021
Cited alongside, same era.
Graph backdoor. In 30th USENIX Security Symposium (USENIX Security 21) . 1523–1540
Zhaohan Xi, Ren Pang, Shouling Ji, and Ting Wang. 2021 · 2021
Cited alongside, same era.
Explainability-based backdoor attacks against graph neural networks. In Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning . 31–36
Jing Xu, Minhui Xue, and Stjepan Picek. 2021 · 2021
Cited alongside, same era.
Backdoor attacks to graph neural networks. In Proceedings of the 26th ACM Symposium on Access Control Models and Technologies . 15–26
Rab: Provable robustness against backdoor attacks. In 2023 IEEE Symposium on Security and Privacy (SP) . IEEE, 1311–1328
Maurice Weber, Xiaojun Xu, Bojan Karlaš, Ce Zhang, and Bo Li. 2023 · 2023
Later among the works it cites.
Rethinking the trigger-injecting position in graph backdoor attack
Jing Xu, Gorka Abad, and Stjepan Picek. 2023 · 2023
Later among the works it cites.
Black-Box Graph Backdoor Defense. In International Conference on Algorithms and Architectures for Parallel Processing . Springer, 163–180
Xiao Yang, Gaolei Li, Xiaoyi Tao, Chaofeng Zhang, and Jianhua Li. 2023 · 2023
Later among the works it cites.
Graph contrastive backdoor attacks. In International Conference on Machine Learning . PMLR, 40888–40910
Hangfan Zhang, Jinghui Chen, Lu Lin, Jinyuan Jia, and Dinghao Wu. 2023 · 2023
Later among the works it cites.
Motif-backdoor: Rethinking the backdoor attack on graph neural networks via motifs
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Zaixi Zhang, Jinyuan Jia, Binghui Wang, and Neil Zhenqiang Gong. 2021 · 2021
Cited alongside, same era.
Neighboring backdoor attacks on graph convolutional network
Liang Chen, Qibiao Peng, Jintang Li, Yang Liu, Jiawei Chen, Yong Li, and Zibin Zheng. 2022a · 2022
Cited alongside, same era.
A General Backdoor Attack to Graph Neural Networks Based on Explanation Method. In 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) . IEEE, 759–768
Luyao Chen, Na Yan, Boyang Zhang, Zhaoyang Wang, Yu Wen, and Yanfei Hu. 2022b · 2022
Cited alongside, same era.
Defending against backdoor attack on graph nerual network by explainability
Bingchen Jiang and Zhao Li. 2022 · 2022
Cited alongside, same era.
Poster: Clean-label backdoor attack on graph neural networks. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security . 3491–3493
Jing Xu and Stjepan Picek. 2022 · 2022
Cited alongside, same era.
Transferable graph backdoor attack. In Proceedings of the 25th International Symposium on Research in Attacks, Intrusions and Defenses . 321–332
Shuiqiao Yang, Bao Gia Doan, Paul Montague, Olivier De Vel, Tamas Abraham, Seyit Camtepe, Damith C Ranasinghe, and Salil S Kanhere. 2022 · 2022
Cited alongside, same era.
Unnoticeable backdoor attacks on graph neural networks. In Proceedings of the ACM Web Conference 2023 . 2263–2273
Enyan Dai, Minhua Lin, Xiang Zhang, and Suhang Wang. 2023 · 2023
Cited alongside, same era.
A semantic backdoor attack against Graph Convolutional Networks
Jiazhu Dai and Zhipeng Xiong. 2023 · 2023
Cited alongside, same era.
Haibin Zheng, Haiyang Xiong, Jinyin Chen, Haonan Ma, and Guohan Huang. 2023 · 2023
Later among the works it cites.
Coca: Improving and Explaining Graph Neural Network-Based Vulnerability Detection Systems. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering . 1–13
Sicong Cao, Xiaobing Sun, Xiaoxue Wu, David Lo, Lili Bo, Bin Li, and Wei Liu. 2024 · 2024
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Securing GNNs: Explanation-Based Identification of Backdoored Training Graphs
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Dongsheng Luo, Tianxiang Zhao, Wei Cheng, Dongkuan Xu, Feng Han, Wenchao Yu, Xiao Liu, Haifeng Chen, and Xiang Zhang. 2024 · 2024
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Distribution preserving backdoor attack in self-supervised learning. In 2024 IEEE Symposium on Security and Privacy (SP) . IEEE Computer Society, 29–29
Guanhong Tao, Zhenting Wang, Shiwei Feng, Guangyu Shen, Shiqing Ma, and Xiangyu Zhang. 2023 · 2024
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Explanatory subgraph attacks against Graph Neural Networks
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Multi-target label backdoor attacks on graph neural networks
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Jiale Zhang, Hao Sui, Xiaobing Sun, Chunpeng Ge, Lu Zhou, and Willy Susilo. 2024c · 2024
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