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Graph prompt tuning has emerged as a promising paradigm to effectively transfer general graph knowledge from pre-trained models to various downstream tasks, particularly in few-shot contexts.
2016
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Z. Yang, W. W. Cohen, and R. Salakhutdinov, “Revisiting semi-supervised learning with graph embeddings,” in Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48 , ser. ICML’16. JMLR.org, 2016, p. 40–48
2016
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T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in International Conference on Learning Representations , 2017. [Online]. Available: https://openreview.net/forum?id=SJU4ayYgl
2017
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P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=rJXMpikCZ
2018
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W. Fan, Y. Ma, Q. Li, Y. He, E. Zhao, J. Tang, and D. Yin, “Graph neural networks for social recommendation,” in The World Wide Web Conference , ser. WWW ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 417–426. [Online]. Available: https://doi.org/10.1145/3308558.3313488
2019
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T. Gu, K. Liu, B. Dolan-Gavitt, and S. Garg, “Badnets: Evaluating backdooring attacks on deep neural networks,” IEEE Access , vol. 7, pp. 47 230–47 244, 2019
2019
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O. Shchur, M. Mumme, A. Bojchevski, and S. Günnemann, “Pitfalls of graph neural network evaluation,” 2019
2019
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B. Wang, Y. Yao, S. Shan, H. Li, B. Viswanath, H. Zheng, and B. Y. Zhao, “Neural cleanse: Identifying and mitigating backdoor attacks in neural networks,” in 2019 IEEE Symposium on Security and Privacy (SP) , 2019, pp. 707–723
2019
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H. Wu, C. Wang, Y. Tyshetskiy, A. Docherty, K. Lu, and L. Zhu, “Adversarial examples for graph data: Deep insights into attack and defense,” in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19 . International Joint Conferences on Artificial Intelligence Organization, 7 2019, pp. 4816–4823. [Online]. Available: https://doi.org/10.24963/ijcai.2019/669
2019
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N. Entezari, S. A. Al-Sayouri, A. Darvishzadeh, and E. E. Papalexakis, “All you need is low (rank): Defending against adversarial attacks on graphs,” in Proceedings of the 13th International Conference on Web Search and Data Mining , ser. WSDM ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 169–177. [Online]. Available: https://doi.org/10.1145/3336191.3371789
2020
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X. Tang, H. Yao, Y. Sun, Y. Wang, J. Tang, C. Aggarwal, P. Mitra, and S. Wang, “Investigating and mitigating degree-related biases in graph convoltuional networks,” in Proceedings of the 29th ACM International Conference on Information & Knowledge Management , ser. CIKM ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 1435–1444. [Online]. Available: https://doi.org/10.1145/3340531.3411872
2020
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Y. You, T. Chen, Y. Sui, T. Chen, Z. Wang, and Y. Shen, “Graph contrastive learning with augmentations,” in Proceedings of the 34th International Conference on Neural Information Processing Systems , ser. NIPS ’20. Red Hook, NY, USA: Curran Associates Inc., 2020
2020
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X. Zhang and M. Zitnik, “Gnnguard: Defending graph neural networks against adversarial attacks,” in NeurIPS , 2020
2020
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Y. Shi, Z. Huang, S. Feng, H. Zhong, W. Wang, and Y. Sun, “Masked label prediction: Unified message passing model for semi-supervised classification,” in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 , Z.-H. Zhou, Ed. International Joint Conferences on Artificial Intelligence Organization, 8 2021, pp. 1548–1554, main Track. [Online]. Available: https://doi.org/10.24963/ijcai.2021/214
2021
Earlier work this paper cites.
Z. Xi, R. Pang, S. Ji, and T. Wang, “Graph backdoor,” in 30th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 21) , 2021
2021
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J. Xu, M. J. Xue, and S. Picek, “Explainability-based backdoor attacks against graph neural networks,” in Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning , ser. WiseML ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 31–36. [Online]. Available: https://doi.org/10.1145/3468218.3469046
2021
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Z. Zhang, J. Jia, B. Wang, and N. Z. Gong, “Backdoor attacks to graph neural networks,” in Proceedings of the 26th ACM Symposium on Access Control Models and Technologies , ser. SACMAT ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 15–26. [Online]. Available: https://doi.org/10.1145/3450569.3463560
2021
Cited alongside, same era.
Y. Chen, H. Yang, Y. Zhang, M. KAILI, T. Liu, B. Han, and J. Cheng, “Understanding and improving graph injection attack by promoting unnoticeability,” in International Conference on Learning Representations , 2022. [Online]. Available: https://openreview.net/forum?id=wkMG8cdvh7-
2022
Cited alongside, same era.
M. M. Li, K. Huang, and M. Zitnik, “Graph representation learning in biomedicine and healthcare,” Nature Biomedical Engineering , vol. 6, pp. 1353 – 1369, 2022. [Online]. Available: https://api.semanticscholar.org/CorpusID:253245343
2022
Cited alongside, same era.
S. Luan, C. Hua, M. Xu, Q. Lu, J. Zhu, X.-W. Chang, J. Fu, J. Leskovec, and D. Precup, “When do graph neural networks help with node classification? investigating the homophily principle on node distinguishability,” in Thirty-seventh Conference on Neural Information Processing Systems , 2023. [Online]. Available: https://openreview.net/forum?id=kJmYu3Ti2z
2023
Later among the works it cites.
L. Peng, S. Cai, Z. Wu, H. Shang, X. Zhu, and X. Li, “Mmgpl: Multimodal medical data analysis with graph prompt learning,” 2023
2023
Later among the works it cites.
X. Sun, H. Cheng, J. Li, B. Liu, and J. Guan, “All in one: Multi-task prompting for graph neural networks,” in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , ser. KDD ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 2120–2131. [Online]. Available: https://doi.org/10.1145/3580305.3599256
2023
Later among the works it cites.
X. Sun, J. Zhang, X. Wu, H. Cheng, Y. Xiong, and J. Li, “Graph prompt learning: A comprehensive survey and beyond,” 2023
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2022
Cited alongside, same era.
M. Sun, K. Zhou, X. He, Y. Wang, and X. Wang, “Gppt: Graph pre-training and prompt tuning to generalize graph neural networks,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , ser. KDD ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 1717–1727. [Online]. Available: https://doi.org/10.1145/3534678.3539249
2022
Cited alongside, same era.
J. Tang, J. Li, Z. Gao, and J. Li, “Rethinking graph neural networks for anomaly detection,” in International Conference on Machine Learning , 2022
2022
Cited alongside, same era.
S. Yang, B. G. Doan, P. Montague, O. De Vel, T. Abraham, S. Camtepe, D. C. Ranasinghe, and S. S. Kanhere, “Transferable graph backdoor attack,” in Proceedings of the 25th International Symposium on Research in Attacks, Intrusions and Defenses , ser. RAID ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 321–332. [Online]. Available: https://doi.org/10.1145/3545948.3545976
2022
Cited alongside, same era.
E. Dai, M. Lin, X. Zhang, and S. Wang, “Unnoticeable backdoor attacks on graph neural networks,” in Proceedings of the ACM Web Conference 2023 , ser. WWW ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 2263–2273. [Online]. Available: https://doi.org/10.1145/3543507.3583392
2023
Cited alongside, same era.
T. Fang, Y. Zhang, Y. YANG, C. Wang, and L. Chen, “Universal prompt tuning for graph neural networks,” in Advances in Neural Information Processing Systems , A. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, Eds., vol. 36. Curran Associates, Inc., 2023, pp. 52 464–52 489. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2023/file/a4a1ee071ce0fe63b83bce507c9dc4d7-Paper-Conference.pdf
2023
Cited alongside, same era.
X. Gong, Y. Chen, W. Yang, H. Huang, and Q. Wang, “B3: Backdoor attacks against black-box machine learning models,” ACM Trans. Priv. Secur. , vol. 26, no. 4, aug 2023. [Online]. Available: https://doi.org/10.1145/3605212
2023
Cited alongside, same era.
Q. Huang, H. Ren, P. Chen, G. Kržmanc, D. Zeng, P. Liang, and J. Leskovec, “PRODIGY: Enabling in-context learning over graphs,” in Thirty-seventh Conference on Neural Information Processing Systems , 2023. [Online]. Available: https://openreview.net/forum?id=pLwYhNNnoR
2023
Cited alongside, same era.
F. Ji, S. H. Lee, H. Meng, K. Zhao, J. Yang, and W. P. Tay, “Leveraging label non-uniformity for node classification in graph neural networks,” in Proceedings of the 40th International Conference on Machine Learning , ser. ICML’23. JMLR.org, 2023
2023
Cited alongside, same era.
2023
Later among the works it cites.
Z. Tan, R. Guo, K. Ding, and H. Liu, “Virtual node tuning for few-shot node classification,” in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , ser. KDD ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 2177–2188. [Online]. Available: https://doi.org/10.1145/3580305.3599541
2023
Later among the works it cites.
J. Xu and S. Picek, “Poster: Multi-target & multi-trigger backdoor attacks on graph neural networks,” in Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security , ser. CCS ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 3570–3572. [Online]. Available: https://doi.org/10.1145/3576915.3624387
2023
Later among the works it cites.
Z. Yi, I. Ounis, and C. MacDonald, “Contrastive graph prompt-tuning for cross-domain recommendation,” ACM Trans. Inf. Syst. , vol. 42, no. 2, dec 2023. [Online]. Available: https://doi.org/10.1145/3618298
2023
Later among the works it cites.
H. Zhang, J. Chen, L. Lin, J. Jia, and D. Wu, “Graph contrastive backdoor attacks,” in Proceedings of the 40th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, and J. Scarlett, Eds., vol. 202. PMLR, 23–29 Jul 2023, pp. 40 888–40 910. [Online]. Available: https://proceedings.mlr.press/v202/zhang23e.html
2023
Later among the works it cites.
K. D. Doan, Y. Lao, and P. Li, “Marksman backdoor: backdoor attacks with arbitrary target class,” in Proceedings of the 36th International Conference on Neural Information Processing Systems , ser. NIPS ’22. Red Hook, NY, USA: Curran Associates Inc., 2024
2024
Closest in time.
S. Ennadir, Y. Abbahaddou, J. F. Lutzeyer, M. Vazirgiannis, and H. Boström, “A simple and yet fairly effective defense for graph neural networks,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 19, pp. 21 063–21 071, Mar. 2024. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/30098
2024
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K. Wang, H. Deng, Y. Xu, Z. Liu, and Y. Fang, “Multi-target label backdoor attacks on graph neural networks,” Pattern Recognition , vol. 152, p. 110449, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0031320324002000
2024
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T. Wang, Y. Yao, F. Xu, M. Xu, S. An, and T. Wang, “Inspecting prediction confidence for detecting black-box backdoor attacks,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 1, pp. 274–282, Mar. 2024. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/27780
2024
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B. Yao, G. Chen, R. Zou, Y. Lu, J. Li, S. Zhang, Y. Sang, S. Liu, J. Hendler, and D. Wang, “More samples or more prompts? exploring effective in-context sampling for llm few-shot prompt engineering,” 2024
2024
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X. Zhao, H. Wu, and X. Zhang, “Effective backdoor attack on graph neural networks in spectral domain,” IEEE Internet of Things Journal , vol. 11, no. 7, pp. 12 102–12 114, 2024
2024
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H. Zheng, H. Xiong, J. Chen, H. Ma, and G. Huang, “Motif-backdoor: Rethinking the backdoor attack on graph neural networks via motifs,” IEEE Transactions on Computational Social Systems , vol. 11, no. 2, pp. 2479–2493, 2024
2024
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