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The advent of the "pre-train, prompt" paradigm has recently extended its generalization ability and data efficiency to graph representation learning, following its achievements in Natural Language Processing (NLP).
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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, ACL/IJCNLP 2021 . 4582–4597
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Overcoming catastrophic forgetting in graph neural networks. In Proceedings of the AAAI conference on artificial intelligence , Vol. 35. 8653–8661
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Reinforcement learning for combinatorial optimization: A survey
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Decoupling value and policy for generalization in reinforcement learning. In International Conference on Machine Learning . 8787–8798
Roberta Raileanu and Rob Fergus. 2021 · 2021
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Training language models to follow instructions with human feedback
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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 . 1717–1727
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Self-supervised learning of graph neural networks: A unified review
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Universal prompt tuning for graph neural networks
Taoran Fang, Yunchao Zhang, Yang Yang, Chunping Wang, and Lei Chen. 2024 · 2024
Closest in time.
Protein Multimer Structure Prediction via Prompt Learning. In The Twelfth International Conference on Learning Representations
Ziqi Gao, Xiangguo Sun, Zijing Liu, Yu Li, Hong Cheng, and Jia Li. 2024 · 2024
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PSP: Pre-training and Structure Prompt Tuning for Graph Neural Networks. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases . 423–439
Qingqing Ge, Zeyuan Zhao, Yiding Liu, Anfeng Cheng, Xiang Li, Shuaiqiang Wang, and Dawei Yin. 2024 · 2024
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Self-pro: A Self-prompt and Tuning Framework for Graph Neural Networks. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases . 197–215
Chenghua Gong, Xiang Li, Jianxiang Yu, Yao Cheng, Jiaqi Tan, and Chengcheng Yu. 2024 · 2024
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Urban Region Pre-training and Prompting: A Graph-based Approach
Jiahui Jin, Yifan Song, Dong Kan, Haojia Zhu, Xiangguo Sun, Zhicheng Li, Xigang Sun, and Jinghui Zhang. 2024 · 2024
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MAG-GNN: Reinforcement Learning Boosted Graph Neural Network
Lecheng Kong, Jiarui Feng, Hao Liu, Dacheng Tao, Yixin Chen, and Muhan Zhang. 2024 · 2024
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Subgraph-level Universal Prompt Tuning
Junhyun Lee, Wooseong Yang, and Jaewoo Kang. 2024 · 2024
Closest in time.
Ddiprompt: Drug-drug interaction event prediction based on graph prompt learning. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management . 2431–2441
Yingying Wang, Yun Xiong, Xixi Wu, Xiangguo Sun, Jiawei Zhang, and GuangYong Zheng. 2024 · 2024
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GraphCBAL: Class-Balanced Active Learning for Graph Neural Networks via Reinforcement Learning. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management . 3022–3031
Chengcheng Yu, Jiapeng Zhu, and Xiang Li. 2024 · 2024
Closest in time.
Sugar: Subgraph neural network with reinforcement pooling and self-supervised mutual information mechanism. In Proceedings of the Web Conference 2021 . 2081–2091
Qingyun Sun, Jianxin Li, Hao Peng, Jia Wu, Yuanxing Ning, Philip S Yu, and Lifang He. 2021 · 2091
Closest in time.