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Graph Prompt Learning (GPL) bridges significant disparities between pretraining and downstream applications to alleviate the knowledge transfer bottleneck in real-world graph learning.
Protein function prediction via graph kernels
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Backdoor Attack of Graph Neural Networks Based on Subgraph Trigger. In Collaborative Computing: Networking, Applications and Worksharing - 17th EAI International Conference, CollaborateCom 2021, Virtual Event, October 16-18, 2021, Proceedings, Part II (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Vol. 407) . Springer, 276–296
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BadPrompt: Backdoor Attacks on Continuous Prompts. In Advances in Neural Information Processing Systems , Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho (Eds.)
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Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
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GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks. In Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023 . ACM, 417–428
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HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks
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BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022 . OpenReview.net
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Prompt Tuning for Graph Neural Networks
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Poster: Clean-label Backdoor Attack on Graph Neural Networks. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, CCS 2022, Los Angeles, CA, USA, November 7-11, 2022 . ACM, 3491–3493
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ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt
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Unnoticeable Backdoor Attacks on Graph Neural Networks. In Proceedings of the ACM Web Conference 2023 (Austin, TX, USA) (WWW ’23) . Association for Computing Machinery, New York, NY, USA, 2263–2273
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UOR: Universal Backdoor Attacks on Pre-trained Language Models
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Universal Prompt Tuning for Graph Neural Networks. In Thirty-seventh Conference on Neural Information Processing Systems
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NOTABLE: Transferable Backdoor Attacks Against Prompt-based NLP Models. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2023, Toronto, Canada, July 9-14, 2023 . Association for Computational Linguistics, 15551–15565
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Deep Prompt Tuning for Graph Transformers
Reza Shirkavand and Heng Huang. 2023 · 2023
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Graph Prompt Learning: A Comprehensive Survey and Beyond
Xiangguo Sun, Jiawen Zhang, Xixi Wu, Hong Cheng, Yun Xiong, and Jia Li. 2023b · 2023
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Virtual Node Tuning for Few-shot Node Classification. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6-10, 2023 . ACM, 2177–2188
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PoisonPrompt: Backdoor Attack on Prompt-based Large Language Models
Hongwei Yao, Jian Lou, and Zhan Qin. 2023 · 2023
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Graph Contrastive Backdoor Attacks. In International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA (Proceedings of Machine Learning Research, Vol. 202) . PMLR, 40888–40910
Hangfan Zhang, Jinghui Chen, Lu Lin, Jinyuan Jia, and Dinghao Wu. 2023a · 2023
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Cross-Domain Few-Shot Graph Classification with a Reinforced Task Coordinator. In Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, IAAI 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2023, Washington, DC, USA, February 7-14, 2023 . AAAI Press, 4893–4901
Qiannan Zhang, Shichao Pei, Qiang Yang, Chuxu Zhang, Nitesh V. Chawla, and Xiangliang Zhang. 2023b · 2023
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Prompt as Triggers for Backdoor Attack: Examining the Vulnerability in Language Models
Shuai Zhao and Jinming Wen. 2023 · 2023
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