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InfoGraph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. In ICLR."},{"key":"e_1_3_2_1_47_1","first-page":"1717","article-title":"GPPT: Graph pre-training and prompt tuning to generalize graph neural networks","author":"Sun Mingchen","year":"2022","unstructured":"Mingchen Sun, Kaixiong Zhou, Xin He, Ying Wang, and Xin Wang. 2022. GPPT: Graph pre-training and prompt tuning to generalize graph neural networks. In KDD. 1717-1727.","journal-title":"KDD."},{"key":"e_1_3_2_1_48_1","volume-title":"Information-Theoretic Foundations and Advances in Graph Machine Learning: A Comprehensive Survey. Authorea Preprints","author":"Sun Qingyun","year":"2026","unstructured":"Qingyun Sun, Yi Huang, Haonan Yuan, Xingcheng Fu, Yisen Gao, Jia Wu, Shujian Yu, Angsheng Li, Jianxin Li, and Philip S Yu. 2026. Information-Theoretic Foundations and Advances in Graph Machine Learning: A Comprehensive Survey. Authorea Preprints (2026)."},{"key":"e_1_3_2_1_49_1","volume-title":"DyG-RAG: Dynamic graph retrieval-augmented generation with event-centric reasoning. arXiv preprint arXiv:2507.13396","author":"Sun Qingyun","year":"2025","unstructured":"Qingyun Sun, Jiaqi Yuan, Shan He, Xiao Guan, Haonan Yuan, Xingcheng Fu, Jianxin Li, and Philip S Yu. 2025. DyG-RAG: Dynamic graph retrieval-augmented generation with event-centric reasoning. arXiv preprint arXiv:2507.13396 (2025)."},{"key":"e_1_3_2_1_50_1","volume-title":"Dynamics of directed graphs: the world-wide Web. Physica A: Statistical Mechanics and its Applications","author":"Tadi\u0107 Bosiljka","year":"2001","unstructured":"Bosiljka Tadi\u0107. 2001. Dynamics of directed graphs: the world-wide Web. Physica A: Statistical Mechanics and its Applications, Vol. 293, 1-2 (2001), 273-284."},{"key":"e_1_3_2_1_51_1","volume-title":"NeurIPS","volume":"36","author":"Tan Yanchao","year":"2024","unstructured":"Yanchao Tan, Zihao Zhou, Hang Lv, Weiming Liu, and Carl Yang. 2024. WalkLM: A uniform language model fine-tuning framework for attributed graph embedding. NeurIPS, Vol. 36 (2024)."},{"key":"e_1_3_2_1_52_1","first-page":"2842","article-title":"HiGPT: Heterogeneous graph language model","author":"Tang Jiabin","year":"2024","unstructured":"Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Long Xia, Dawei Yin, and Chao Huang. 2024b. HiGPT: Heterogeneous graph language model. In KDD. 2842-2853.","journal-title":"KDD."},{"key":"e_1_3_2_1_53_1","volume-title":"Cross-domain graph data scaling: a showcase with diffusion models. arXiv preprint arXiv:2406.01899","author":"Tang Wenzhuo","year":"2024","unstructured":"Wenzhuo Tang, Haitao Mao, Danial Dervovic, Ivan Brugere, Saumitra Mishra, Yuying Xie, and Jiliang Tang. 2024a. Cross-domain graph data scaling: a showcase with diffusion models. arXiv preprint arXiv:2406.01899 (2024)."},{"key":"e_1_3_2_1_54_1","volume-title":"Multihop-RAG: Benchmarking retrieval-augmented generation for multi-hop queries. arXiv preprint arXiv:2401.15391","author":"Tang Yixuan","year":"2024","unstructured":"Yixuan Tang and Yi Yang. 2024. Multihop-RAG: Benchmarking retrieval-augmented generation for multi-hop queries. arXiv preprint arXiv:2401.15391 (2024)."},{"key":"e_1_3_2_1_55_1","unstructured":"Qwen Team et al. 2024. 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Genome Medicine (2026)."},{"key":"e_1_3_2_1_68_1","first-page":"16962","article-title":"Self-supervised heterogeneous graph pre-training based on structural clustering","volume":"35","author":"Yang Yaming","year":"2022","unstructured":"Yaming Yang, Ziyu Guan, Zhe Wang, Wei Zhao, Cai Xu, Weigang Lu, and Jianbin Huang. 2022. Self-supervised heterogeneous graph pre-training based on structural clustering. NeurIPS, Vol. 35 (2022), 16962-16974.","journal-title":"NeurIPS"},{"key":"e_1_3_2_1_69_1","unstructured":"Gustavo Ye. 2024. nano-vectordb. https:\/\/github.com\/gusye1234\/nano-vectordb"},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/3592099"},{"key":"e_1_3_2_1_71_1","first-page":"35277","article-title":"Train once and explain everywhere: Pre-training interpretable graph neural networks","volume":"36","author":"Yin Jun","year":"2023","unstructured":"Jun Yin, Chaozhuo Li, Hao Yan, Jianxun Lian, and Senzhang Wang. 2023. Train once and explain everywhere: Pre-training interpretable graph neural networks. NeurIPS, Vol. 36 (2023), 35277-35299.","journal-title":"NeurIPS"},{"key":"e_1_3_2_1_72_1","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"33","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. NeurIPS, Vol. 33 (2020), 5812-5823.","journal-title":"NeurIPS"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i15.29596"},{"key":"e_1_3_2_1_74_1","first-page":"1142","article-title":"Samgpt: Text-free graph foundation model for multi-domain pre-training and cross-domain adaptation","author":"Yu Xingtong","year":"2025","unstructured":"Xingtong Yu, Zechuan Gong, Chang Zhou, Yuan Fang, and Hui Zhang. 2025. Samgpt: Text-free graph foundation model for multi-domain pre-training and cross-domain adaptation. In WWW. 1142-1153.","journal-title":"WWW."},{"key":"e_1_3_2_1_75_1","volume-title":"Text-free multi-domain graph pre-training: toward graph foundation models. arXiv preprint arXiv:2405.13934","author":"Yu Xingtong","year":"2024","unstructured":"Xingtong Yu, Chang Zhou, Yuan Fang, and Xinming Zhang. 2024b. Text-free multi-domain graph pre-training: toward graph foundation models. arXiv preprint arXiv:2405.13934 (2024)."},{"key":"e_1_3_2_1_76_1","first-page":"49715","article-title":"Environment-aware dynamic graph learning for out-of-distribution generalization","volume":"36","author":"Yuan Haonan","year":"2023","unstructured":"Haonan Yuan, Qingyun Sun, Xingcheng Fu, Ziwei Zhang, Cheng Ji, Hao Peng, and Jianxin Li. 2023. Environment-aware dynamic graph learning for out-of-distribution generalization. NeurIPS, Vol. 36 (2023), 49715-49747.","journal-title":"NeurIPS"},{"key":"e_1_3_2_1_77_1","volume-title":"GRAVER: Generative graph vocabularies for robust graph foundation models fine-tuning. In NeurIPS.","author":"Yuan Haonan","year":"2025","unstructured":"Haonan Yuan, Qingyun Sun, Junhua Shi, Xingcheng Fu, Bryan Hooi, Jianxin Li, and Philip S Yu. 2025a. GRAVER: Generative graph vocabularies for robust graph foundation models fine-tuning. In NeurIPS."},{"key":"e_1_3_2_1_78_1","unstructured":"Haonan Yuan Qingyun Sun Junhua Shi Xingcheng Fu Bryan Hooi Jianxin Li and Philip S Yu. 2025b. How much can transfer? BRIDGE: Bounded multi-domain graph foundation model with generalization guarantees. In ICML."},{"key":"e_1_3_2_1_79_1","first-page":"912","article-title":"Knowledge graph reasoning with relational digraph","author":"Zhang Yongqi","year":"2022","unstructured":"Yongqi Zhang and Quanming Yao. 2022. Knowledge graph reasoning with relational digraph. In WWW. 912-924.","journal-title":"WWW."},{"key":"e_1_3_2_1_80_1","first-page":"15870","article-title":"Motif-based graph self-supervised learning for molecular property prediction","volume":"34","author":"Zhang Zaixi","year":"2021","unstructured":"Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Chee-Kong Lee. 2021. Motif-based graph self-supervised learning for molecular property prediction. NeurIPS, Vol. 34 (2021), 15870-15882.","journal-title":"NeurIPS"},{"key":"e_1_3_2_1_81_1","first-page":"4443","article-title":"All in one and one for all: A simple yet effective method towards cross-domain graph pretraining","author":"Zhao Haihong","year":"2024","unstructured":"Haihong Zhao, Aochuan Chen, Xiangguo Sun, Hong Cheng, and Jia Li. 2024a. All in one and one for all: A simple yet effective method towards cross-domain graph pretraining. In KDD. 4443-4454.","journal-title":"KDD."},{"key":"e_1_3_2_1_82_1","first-page":"28631","article-title":"Continual forgetting for pre-trained vision models","author":"Zhao Hongbo","year":"2024","unstructured":"Hongbo Zhao, Bolin Ni, Junsong Fan, Yuxi Wang, Yuntao Chen, Gaofeng Meng, and Zhaoxiang Zhang. 2024b. Continual forgetting for pre-trained vision models. In CVPR. 28631-28642.","journal-title":"CVPR."},{"key":"e_1_3_2_1_83_1","first-page":"2183","article-title":"GraphCLIP: Enhancing transferability in graph foundation models for text-attributed graphs","author":"Zhu Yun","year":"2025","unstructured":"Yun Zhu, Haizhou Shi, Xiaotang Wang, Yongchao Liu, Yaoke Wang, Boci Peng, Chuntao Hong, and Siliang Tang. 2025. GraphCLIP: Enhancing transferability in graph foundation models for text-attributed graphs. In WWW. 2183-2197.","journal-title":"WWW."}],"event":{"name":"WWW '26: The ACM Web Conference 2026","location":"Dubai United Arab Emirates","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2026"],"original-title":[],"deposited":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:27:00Z","timestamp":1775838420000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774904.3792139"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,12]]},"references-count":83,"alternative-id":["10.1145\/3774904.3792139","10.1145\/3774904"],"URL":"https:\/\/doi.org\/10.1145\/3774904.3792139","relation":{},"subject":[],"published":{"date-parts":[[2026,4,12]]},"assertion":[{"value":"2026-04-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}