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Empowered by the inherent advantages of machine learning, the learning-based techniques show the generalization ability and better performance in many scenarios, including graph data management and graph query processing. Despite the efficiency and accuracy brought by machine learning techniques, machine learning for graph database models still face several critical challenges, including scalability and adaptability. In this tutorial, we first provide an in-depth survey of learning-based graph data management and query processing techniques published in recent database and data mining conferences to sketch the frontier of the research of Machine Learning for Graph Database. We also discuss the open challenges and provide future directions.<\/jats:p>","DOI":"10.14778\/3750601.3750702","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:37:51Z","timestamp":1758029871000},"page":"5499-5503","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning for Graph Data Management and Query Processing"],"prefix":"10.14778","volume":"18","author":[{"given":"Hanchen","family":"Wang","sequence":"first","affiliation":[{"name":"University of Technology Sydney, Sydney, Australia"}]},{"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"Common Prosperity Visualization and Policy Simulation Lab of Zhejiang, Gongshang University, Hangzhou, China"}]},{"given":"Wenjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of New South Wales, Sydney, Australia"}]}],"member":"320","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Yunsheng Bai Hao Ding Song Bian Ting Chen Yizhou Sun and Wei Wang. 2019. 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