{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T17:13:11Z","timestamp":1775841191449,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","funder":[{"name":"Australian Research Council Future Fellowship","award":["FT210100624"],"award-info":[{"award-number":["FT210100624"]}]},{"name":"Australian Research Council Discovery Project","award":["DP240101108"],"award-info":[{"award-number":["DP240101108"]}]},{"name":"Australian Research Council Discovery Project","award":["DP260100326"],"award-info":[{"award-number":["DP260100326"]}]},{"name":"Australian Research Council Linkage Project","award":["LP230200892"],"award-info":[{"award-number":["LP230200892"]}]},{"name":"Australian Research Council Linkage Project","award":["LP240200546"],"award-info":[{"award-number":["LP240200546"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,4,13]]},"DOI":"10.1145\/3774904.3792734","type":"proceedings-article","created":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T21:54:39Z","timestamp":1775771679000},"page":"4919-4928","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Relational Database Distillation: From Structured Tables to Condensed Graph Data"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1146-8925","authenticated-orcid":false,"given":"Xinyi","family":"Gao","sequence":"first","affiliation":[{"name":"The University of Queensland, Brisbane, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7102-7997","authenticated-orcid":false,"given":"Jingxi","family":"Zhang","sequence":"additional","affiliation":[{"name":"The University of Queensland, Brisbane, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7351-7574","authenticated-orcid":false,"given":"Lijian","family":"Chen","sequence":"additional","affiliation":[{"name":"The University of Queensland, Brisbane, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7269-146X","authenticated-orcid":false,"given":"Tong","family":"Chen","sequence":"additional","affiliation":[{"name":"The University of Queensland, Brisbane, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8262-8883","authenticated-orcid":false,"given":"Lizhen","family":"Cui","sequence":"additional","affiliation":[{"name":"Shandong University, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1395-261X","authenticated-orcid":false,"given":"Hongzhi","family":"Yin","sequence":"additional","affiliation":[{"name":"The University of Queensland, Brisbane, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,12]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 4750-4759","author":"Cazenavette George","year":"2022","unstructured":"George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu. 2022. Dataset distillation by matching training trajectories. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 4750-4759."},{"key":"e_1_3_2_1_2_1","volume-title":"Relgnn: Composite message passing for relational deep learning. arXiv preprint arXiv:2502.06784","author":"Chen Tianlang","year":"2025","unstructured":"Tianlang Chen, Charilaos Kanatsoulis, and Jure Leskovec. 2025. Relgnn: Composite message passing for relational deep learning. arXiv preprint arXiv:2502.06784 (2025)."},{"key":"e_1_3_2_1_3_1","volume-title":"Supervised learning on relational databases with graph neural networks. arXiv preprint arXiv:2002.02046","author":"Cvitkovic Milan","year":"2020","unstructured":"Milan Cvitkovic. 2020. Supervised learning on relational databases with graph neural networks. arXiv preprint arXiv:2002.02046 (2020)."},{"key":"e_1_3_2_1_4_1","unstructured":"DB-Engines. 2023. DBMS Popularity Broken Down by Database Model. https:\/\/db-engines.com\/en\/ranking_categories. Accessed: 2023-12-31."},{"key":"e_1_3_2_1_5_1","volume-title":"The Faiss library. arXiv preprint arXiv:2401.08281","author":"Douze Matthijs","year":"2024","unstructured":"Matthijs Douze, Alexandr Guzhva, Chengqi Deng, Jeff Johnson, Gergely Szilvasy, Pierre-Emmanuel Mazar\u00e9, Maria Lomeli, Lucas Hosseini, and Herv\u00e9 J\u00e9gou. 2024. The Faiss library. arXiv preprint arXiv:2401.08281 (2024)."},{"key":"e_1_3_2_1_6_1","volume-title":"Relational Graph Transformer. arXiv preprint arXiv:2505.10960","author":"Dwivedi Vijay Prakash","year":"2025","unstructured":"Vijay Prakash Dwivedi, Sri Jaladi, Yangyi Shen, Federico L\u00f3pez, Charilaos I Kanatsoulis, Rishi Puri, Matthias Fey, and Jure Leskovec. 2025. Relational Graph Transformer. arXiv preprint arXiv:2505.10960 (2025)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7908-2151-2"},{"key":"e_1_3_2_1_8_1","volume-title":"Forty-first International Conference on Machine Learning.","author":"Fey Matthias","year":"2024","unstructured":"Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, and Jure Leskovec. 2024. Position: Relational deep learning-graph representation learning on relational databases. In Forty-first International Conference on Machine Learning."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3362863"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00237"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671917"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3711896.3736892"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3696410.3714916"},{"key":"e_1_3_2_1_14_1","volume-title":"Robgc: Towards robust graph condensation","author":"Gao Xinyi","year":"2025","unstructured":"Xinyi Gao, Hongzhi Yin, Tong Chen, Guanhua Ye, Wentao Zhang, and Bin Cui. 2025c. Robgc: Towards robust graph condensation. IEEE Transactions on Knowledge and Data Engineering (2025)."},{"key":"e_1_3_2_1_15_1","volume-title":"Graph condensation: A survey","author":"Gao Xinyi","year":"2025","unstructured":"Xinyi Gao, Junliang Yu, Tong Chen, Guanhua Ye, Wentao Zhang, and Hongzhi Yin. 2025d. Graph condensation: A survey. IEEE Transactions on Knowledge and Data Engineering (2025)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615055"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE60146.2024.00236"},{"key":"e_1_3_2_1_18_1","volume-title":"International Conference on Machine Learning. PMLR, 1263-1272","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. In International Conference on Machine Learning. PMLR, 1263-1272."},{"key":"e_1_3_2_1_19_1","volume-title":"Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA. 1024-1034."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380027"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467256"},{"key":"e_1_3_2_1_22_1","volume-title":"NeurIPS 2024 Third Table Representation Learning Workshop.","author":"Hudovernik Valter","year":"2024","unstructured":"Valter Hudovernik. 2024. Relational data generation with graph neural networks and latent diffusion models. In NeurIPS 2024 Third Table Representation Learning Workshop."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2750683"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/1391729.1391730"},{"key":"e_1_3_2_1_25_1","first-page":"1","article-title":"A survey and comparison of relational and non-relational database","volume":"1","author":"Jatana Nishtha","year":"2012","unstructured":"Nishtha Jatana, Sahil Puri, Mehak Ahuja, Ishita Kathuria, and Dishant Gosain. 2012. A survey and comparison of relational and non-relational database. International Journal of Engineering Research & Technology, Vol. 1, 6 (2012), 1-5.","journal-title":"International Journal of Engineering Research & Technology"},{"key":"e_1_3_2_1_26_1","unstructured":"Martin Jurkovic Valter Hudovernik and Erik \u0160trumbelj. [n.d.]. SyntheRela: A Benchmark For Synthetic Relational Database Generation. In Will Synthetic Data Finally Solve the Data Access Problem?"},{"key":"e_1_3_2_1_27_1","volume-title":"Joint Relational Database Generation via Graph-Conditional Diffusion Models. arXiv preprint arXiv:2505.16527","author":"Ketata Mohamed Amine","year":"2025","unstructured":"Mohamed Amine Ketata, David L\u00fcdke, Leo Schwinn, and Stephan G\u00fcnnemann. 2025. Joint Relational Database Generation via Graph-Conditional Diffusion Models. arXiv preprint arXiv:2505.16527 (2025)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41109-019-0232-2"},{"key":"e_1_3_2_1_29_1","volume-title":"Graph neural networks for databases: A survey. arXiv preprint arXiv:2502.12908","author":"Li Ziming","year":"2025","unstructured":"Ziming Li, Youhuan Li, Yuyu Luo, Guoliang Li, and Chuxu Zhang. 2025. Graph neural networks for databases: A survey. arXiv preprint arXiv:2502.12908 (2025)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE65448.2025.00132"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2025.3589596"},{"key":"e_1_3_2_1_32_1","first-page":"13877","article-title":"Efficient dataset distillation using random feature approximation","volume":"35","author":"Loo Noel","year":"2022","unstructured":"Noel Loo, Ramin Hasani, Alexander Amini, and Daniela Rus. 2022. Efficient dataset distillation using random feature approximation. Advances in Neural Information Processing Systems, Vol. 35 (2022), 13877-13891.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_33_1","unstructured":"Timothy Nguyen Zhourong Chen and Jaehoon Lee. 2021. Dataset Meta-Learning from Kernel Ridge-Regression. In ICLR."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2016.49"},{"key":"e_1_3_2_1_35_1","first-page":"21330","article-title":"Relbench: A benchmark for deep learning on relational databases","volume":"37","author":"Robinson Joshua","year":"2024","unstructured":"Joshua Robinson, Rishabh Ranjan, Weihua Hu, Kexin Huang, Jiaqi Han, Alejandro Dobles, Matthias Fey, Jan Eric Lenssen, Yiwen Yuan, Zecheng Zhang, et al., 2024. Relbench: A benchmark for deep learning on relational databases. Advances in Neural Information Processing Systems, Vol. 37 (2024), 21330-21341.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3712589"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3696410.3714921"},{"key":"e_1_3_2_1_39_1","first-page":"41186","article-title":"Wikidbs: A large-scale corpus of relational databases from wikidata","volume":"37","author":"Vogel Liane","year":"2024","unstructured":"Liane Vogel, Jan-Micha Bodensohn, and Carsten Binnig. 2024. Wikidbs: A large-scale corpus of relational databases from wikidata. Advances in Neural Information Processing Systems, Vol. 37 (2024), 41186-41201.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_40_1","volume-title":"Dataset distillation. ArXiv preprint","author":"Wang Tongzhou","year":"2018","unstructured":"Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros. 2018. Dataset distillation. ArXiv preprint (2018)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Xiao Wang Houye Ji Chuan Shi Bai Wang Yanfang Ye Peng Cui and Philip S Yu. 2019a. Heterogeneous graph attention network. In The world wide web conference. 2022-2032.","DOI":"10.1145\/3308558.3313562"},{"key":"e_1_3_2_1_42_1","volume-title":"Dynamic graph cnn for learning on point clouds. ACM Transactions on Graphics (tog)","author":"Wang Yue","year":"2019","unstructured":"Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. 2019b. Dynamic graph cnn for learning on point clouds. ACM Transactions on Graphics (tog), Vol. 38, 5 (2019), 1-12."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3726302.3730003"},{"key":"e_1_3_2_1_44_1","volume-title":"When Graph Contrastive Learning Backfires: Spectral Vulnerability and Defense in Recommendation. ACM Transactions on Information Systems","author":"Wang Zongwei","year":"2025","unstructured":"Zongwei Wang, Min Gao, Junliang Yu, Shazia Sadiq, Hongzhi Yin, and Ling Liu. 2025b. When Graph Contrastive Learning Backfires: Spectral Vulnerability and Defense in Recommendation. ACM Transactions on Information Systems (2025)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00602"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671795"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553517"},{"key":"e_1_3_2_1_48_1","volume-title":"How powerful are graph neural networks? arXiv preprint arXiv:1810.00826","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-0748-4"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/2699670"},{"key":"e_1_3_2_1_51_1","volume-title":"On-device recommender systems: A comprehensive survey. Data Science and Engineering","author":"Yin Hongzhi","year":"2025","unstructured":"Hongzhi Yin, Liang Qu, Tong Chen, Wei Yuan, Ruiqi Zheng, Jing Long, Xin Xia, Yuhui Shi, and Chengqi Zhang. 2025. On-device recommender systems: A comprehensive survey. Data Science and Engineering (2025), 1-30."},{"key":"e_1_3_2_1_52_1","volume-title":"Dataset distillation: A comprehensive review","author":"Yu Ruonan","year":"2023","unstructured":"Ruonan Yu, Songhua Liu, and Xinchao Wang. 2023. Dataset distillation: A comprehensive review. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)."},{"key":"e_1_3_2_1_53_1","volume-title":"Gfs: Graph-based feature synthesis for prediction over relational databases. arXiv preprint arXiv:2312.02037","author":"Zhang Han","year":"2023","unstructured":"Han Zhang, Quan Gan, David Wipf, and Weinan Zhang. 2023. Gfs: Graph-based feature synthesis for prediction over relational databases. arXiv preprint arXiv:2312.02037 (2023)."},{"key":"e_1_3_2_1_54_1","volume-title":"A survey on graph structure learning: Progress and opportunities. arXiv preprint arXiv:2103.03036","author":"Zhu Yanqiao","year":"2021","unstructured":"Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Yuanqi Du, Jieyu Zhang, Qiang Liu, Carl Yang, and Shu Wu. 2021. A survey on graph structure learning: Progress and opportunities. arXiv preprint arXiv:2103.03036 (2021)."}],"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":"2026-04-10T16:30:47Z","timestamp":1775838647000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774904.3792734"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,12]]},"references-count":54,"alternative-id":["10.1145\/3774904.3792734","10.1145\/3774904"],"URL":"https:\/\/doi.org\/10.1145\/3774904.3792734","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"}}]}}