{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T13:06:27Z","timestamp":1785503187709,"version":"3.56.0"},"publisher-location":"New York, NY, USA","reference-count":43,"publisher":"ACM","funder":[{"name":"University Grants Committee","award":["17203320"],"award-info":[{"award-number":["17203320"]}]},{"name":"University Grants Committee","award":["17209822"],"award-info":[{"award-number":["17209822"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,9]]},"DOI":"10.1145\/3770854.3780291","type":"proceedings-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T12:07:40Z","timestamp":1785499660000},"page":"1673-1682","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning and Editing Universal Graph Prompt Tuning via Reinforcement Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7876-3740","authenticated-orcid":false,"given":"Jinfeng","family":"Xu","sequence":"first","affiliation":[{"name":"The University of Hong Kong, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5779-3523","authenticated-orcid":false,"given":"Zheyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1638-9623","authenticated-orcid":false,"given":"Shuo","family":"Yang","sequence":"additional","affiliation":[{"name":"The University of Hong Kong, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6749-5442","authenticated-orcid":false,"given":"Jinze","family":"Li","sequence":"additional","affiliation":[{"name":"The University of Hong Kong, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6952-0886","authenticated-orcid":false,"given":"Hewei","family":"Wang","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2118-2280","authenticated-orcid":false,"given":"Yijie","family":"Li","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3454-8731","authenticated-orcid":false,"given":"Edith C. H.","family":"Ngai","sequence":"additional","affiliation":[{"name":"University of Hong Kong, Hong Kong SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems Vol. 33 (2020) 1877-1901."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28666"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-2285"},{"key":"e_1_3_2_2_4_1","volume-title":"Diffusion improves graph learning. Advances in neural information processing systems","author":"Gasteiger Johannes","year":"2019","unstructured":"Johannes Gasteiger, Stefan Wei\u00dfenberger, and Stephan G\u00fcnnemann. 2019. Diffusion improves graph learning. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_5_1","volume-title":"Why generalization in rl is difficult: Epistemic pomdps and implicit partial observability. Advances in neural information processing systems","author":"Ghosh Dibya","year":"2021","unstructured":"Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P Adams, and Sergey Levine. 2021. Why generalization in rl is difficult: Epistemic pomdps and implicit partial observability. Advances in neural information processing systems, Vol. 34 (2021), 25502-25515."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467450"},{"key":"e_1_3_2_2_7_1","first-page":"10174","article-title":"Graph policy network for transferable active learning on graphs","volume":"33","author":"Hu Shengding","year":"2020","unstructured":"Shengding Hu, Zheng Xiong, Meng Qu, Xingdi Yuan, Marc-Alexandre C\u00f4t\u00e9, Zhiyuan Liu, and Jian Tang. 2020b. Graph policy network for transferable active learning on graphs. Advances in Neural Information Processing Systems, Vol. 33 (2020), 10174-10185.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_8_1","volume-title":"Strategies For Pre-training Graph Neural Networks. In International Conference on Learning Representations (ICLR).","author":"Hu W","year":"2020","unstructured":"W Hu, B Liu, J Gomes, M Zitnik, P Liang, V Pande, and J Leskovec. 2020a. Strategies For Pre-training Graph Neural Networks. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"e_1_3_2_2_10_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations.","author":"Kipf Thomas N","year":"2017","unstructured":"Thomas N Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","first-page":"12000","DOI":"10.52202\/075280-0525","article-title":"Mag-gnn: Reinforcement learning boosted graph neural network","volume":"36","author":"Kong Lecheng","year":"2023","unstructured":"Lecheng Kong, Jiarui Feng, Hao Liu, Dacheng Tao, Yixin Chen, and Muhan Zhang. 2023. Mag-gnn: Reinforcement learning boosted graph neural network. Advances in Neural Information Processing Systems, Vol. 36 (2023), 12000-12021.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_12_1","volume-title":"Subgraph-level universal prompt tuning. arXiv preprint arXiv:2402.10380","author":"Lee Junhyun","year":"2024","unstructured":"Junhyun Lee, Wooseong Yang, and Jaewoo Kang. 2024. Subgraph-level universal prompt tuning. arXiv preprint arXiv:2402.10380 (2024)."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i12.29264"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3282989"},{"key":"e_1_3_2_2_15_1","volume-title":"A survey of knowledge graph reasoning on graph types: Static, dynamic, and multi-modal","author":"Liang Ke","year":"2024","unstructured":"Ke Liang, Lingyuan Meng, Meng Liu, Yue Liu, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu, Fuchun Sun, and Kunlun He. 2024. A survey of knowledge graph reasoning on graph types: Static, dynamic, and multi-modal. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024)."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17049"},{"key":"e_1_3_2_2_17_1","volume-title":"prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM computing surveys","author":"Liu Pengfei","year":"2023","unstructured":"Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023b. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM computing surveys, Vol. 55, 9 (2023), 1-35."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583386"},{"key":"e_1_3_2_2_19_1","volume-title":"Streaming social event detection and evolution discovery in heterogeneous information networks. ACM Transactions on Knowledge Discovery from Data (TKDD)","author":"Peng Hao","year":"2021","unstructured":"Hao Peng, Jianxin Li, Yangqiu Song, Renyu Yang, Rajiv Ranjan, Philip S Yu, and Lifang He. 2021. Streaming social event detection and evolution discovery in heterogeneous information networks. ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 15, 5 (2021), 1-33."},{"key":"e_1_3_2_2_20_1","volume-title":"International Conference on Machine Learning. PMLR, 8787-8798","author":"Raileanu Roberta","year":"2021","unstructured":"Roberta Raileanu and Rob Fergus. 2021. Decoupling value and policy for generalization in reinforcement learning. In International Conference on Machine Learning. PMLR, 8787-8798."},{"key":"e_1_3_2_2_21_1","volume-title":"Deep learning for the life sciences: applying deep learning to genomics, microscopy, drug discovery, and more","author":"Ramsundar Bharath","unstructured":"Bharath Ramsundar, Peter Eastman, Pat Walters, and Vijay Pande. 2019. Deep learning for the life sciences: applying deep learning to genomics, microscopy, drug discovery, and more. O'Reilly Media."},{"key":"e_1_3_2_2_22_1","volume-title":"Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347","author":"Schulman John","year":"2017","unstructured":"John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)."},{"key":"e_1_3_2_2_23_1","volume-title":"Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868","author":"Shchur Oleksandr","year":"2018","unstructured":"Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2018. Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868 (2018)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539249"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449822"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599256"},{"key":"e_1_3_2_2_27_1","volume-title":"Deep Graph Infomax. In International Conference on Learning Representations.","author":"Veli\u010dkovi\u0107 Petar","year":"2019","unstructured":"Petar Veli\u010dkovi\u0107, William Fedus, William L Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R Devon Hjelm. 2019. Deep Graph Infomax. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_28_1","first-page":"7968","article-title":"Improving generalization in reinforcement learning with mixture regularization","volume":"33","author":"Wang Kaixin","year":"2020","unstructured":"Kaixin Wang, Bingyi Kang, Jie Shao, and Jiashi Feng. 2020. Improving generalization in reinforcement learning with mixture regularization. Advances in Neural Information Processing Systems, Vol. 33 (2020), 7968-7978.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE55515.2023.00088"},{"key":"e_1_3_2_2_30_1","volume-title":"Forty-second International Conference on Machine Learning.","author":"Wang Qunzhong","year":"2025","unstructured":"Qunzhong Wang, Xiangguo Sun, and Hong Cheng. 2025. Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis. In Forty-second International Conference on Machine Learning."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3131584"},{"key":"e_1_3_2_2_32_1","volume-title":"MoleculeNet: a benchmark for molecular machine learning. Chemical science","author":"Wu Zhenqin","year":"2018","unstructured":"Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande. 2018. MoleculeNet: a benchmark for molecular machine learning. Chemical science, Vol. 9, 2 (2018), 513-530."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679697"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3726302.3729927"},{"key":"e_1_3_2_2_35_1","volume-title":"Multi-modal Dynamic Proxy Learning for Personalized Multiple Clustering. arXiv preprint arXiv:2511.07274","author":"Xu Jinfeng","year":"2025","unstructured":"Jinfeng Xu, Zheyu Chen, Shuo Yang, Jinze Li, Ziyue Peng, Zewei Liu, Hewei Wang, Jiayi Zhang, and Edith CH Ngai. 2025b. Multi-modal Dynamic Proxy Learning for Personalized Multiple Clustering. arXiv preprint arXiv:2511.07274 (2025)."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i12.33408"},{"key":"e_1_3_2_2_37_1","volume-title":"International Conference on Learning Representations.","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00653"},{"key":"e_1_3_2_2_39_1","volume-title":"International Conference on Machine Learning. PMLR, 11975-11986","author":"Yehudai Gilad","year":"2021","unstructured":"Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, and Haggai Maron. 2021. From local structures to size generalization in graph neural networks. In International Conference on Machine Learning. PMLR, 11975-11986."},{"key":"e_1_3_2_2_40_1","volume-title":"Graph contrastive learning with augmentations. Advances in neural information processing systems","author":"You Yuning","year":"2020","unstructured":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. Advances in neural information processing systems, Vol. 33 (2020), 5812-5823."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679624"},{"key":"e_1_3_2_2_42_1","volume-title":"Relief: Reinforcement learning empowered graph feature prompt tuning. arXiv preprint arXiv:2408.03195","author":"Zhu Jiapeng","year":"2024","unstructured":"Jiapeng Zhu, Zichen Ding, Jianxiang Yu, Jiaqi Tan, Xiang Li, and Weining Qian. 2024. Relief: Reinforcement learning empowered graph feature prompt tuning. arXiv preprint arXiv:2408.03195 (2024)."},{"key":"e_1_3_2_2_43_1","volume-title":"Sgl-pt: A strong graph learner with graph prompt tuning. arXiv preprint arXiv:2302.12449","author":"Zhu Yun","year":"2023","unstructured":"Yun Zhu, Jianhao Guo, and Siliang Tang. 2023. Sgl-pt: A strong graph learner with graph prompt tuning. arXiv preprint arXiv:2302.12449 (2023)."}],"event":{"name":"KDD '26: The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Jeju Island Republic of Korea","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3770854.3780291","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T12:19:38Z","timestamp":1785500378000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770854.3780291"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,20]]},"references-count":43,"alternative-id":["10.1145\/3770854.3780291","10.1145\/3770854"],"URL":"https:\/\/doi.org\/10.1145\/3770854.3780291","relation":{},"subject":[],"published":{"date-parts":[[2026,4,20]]},"assertion":[{"value":"2026-04-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}