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In recent years, graph prompt learning/tuning has garnered increasing attention in adapting pre-trained models for graph representation learning.
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2020
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Z. Hu, Y. Dong, K. Wang, K.-W. Chang, and Y. Sun, “Gpt-gnn: Generative pre-training of graph neural networks,” in Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining
2020
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2020
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2020
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2020
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Y. Lu, X. Jiang, Y. Fang, and C. Shi, “Learning to pre-train graph neural networks,” in Proceedings of the AAAI conference on artificial intelligence
2021
Cited alongside, same era.
Z. Liu, X. Yu, Y. Fang, and X. Zhang, “Graphprompt: Unifying pre-training and downstream tasks for graph neural networks,” in Proceedings of the ACM Web Conference 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
2023
Later among the works it cites.
2023
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W. Zhao, Q. Wu, C. Yang, and J. Yan, “Graphglow: Universal and generalizable structure learning for graph neural networks,” in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
2023
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X. L. Li and P. Liang, “Prefix-tuning: Optimizing continuous prompts for generation,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
M. Jia, L. Tang, B.-C. Chen, C. Cardie, S. Belongie, B. Hariharan, and S.-N. Lim, “Visual prompt tuning,” in European Conference on Computer Vision
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
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
J. Xia, L. Wu, J. Chen, B. Hu, and S. Z. Li, “Simgrace: A simple framework for graph contrastive learning without data augmentation,” in Proceedings of the ACM Web Conference 2022
2022
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
2023
Cited alongside, same era.
Y. Guo, C. Yang, Y. Chen, J. Liu, C. Shi, and J. Du, “A data-centric framework to endow graph neural networks with out-of-distribution detection ability,” in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
2023
Later among the works it cites.
2023
Later among the works it cites.
X. Liu, Y. Zheng, Z. Du, M. Ding, Y. Qian, Z. Yang, and J. Tang, “Gpt understands, too,” AI Open
2023
Later among the works it cites.
Q. Huang, X. Dong, D. Chen, W. Zhang, F. Wang, G. Hua, and N. Yu, “Diversity-aware meta visual prompting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
S. Li, X. Han, and J. Bai, “Adaptergnn: Parameter-efficient fine-tuning improves generalization in gnns,” in Proceedings of the AAAI Conference on Artificial Intelligence
2024
Closest in time.
Q. Huang, H. Ren, P. Chen, G. Kržmanc, D. Zeng, P. S. Liang, and J. Leskovec, “Prodigy: Enabling in-context learning over graphs,” Advances in Neural Information Processing Systems
2024
Closest in time.