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Surv."],"published-print":{"date-parts":[[2025,11,30]]},"abstract":"<jats:p>\n                    In recent years, graph representation learning has achieved remarkable success while suffering from low-quality data problems. As a mature technology to improve data quality in computer vision, data augmentation has also attracted increasing attention in graph domain. To advance research in this emerging direction, this survey provides a comprehensive review and summary of existing graph data augmentation (GDAug) techniques. Specifically, this survey first provides an overview of various feasible taxonomies and categorizes existing GDAug studies based on multi-scale graph elements. Subsequently, for each type of GDAug technique, this survey formalizes standardized technical definition, discuss the technical details, and provide schematic illustration. The survey also reviews domain-specific graph data augmentation techniques, including those for heterogeneous graphs, temporal graphs, spatio-temporal graphs, and hypergraphs. In addition, this survey provides a summary of available evaluation metrics and design guidelines for graph data augmentation. Lastly, it outlines the applications of GDAug at both the data and model levels, discusses open issues in the field, and looks forward to future directions. The latest advances in GDAug are summarized in GitHub.\n                    <jats:xref ref-type=\"fn\">\n                      <jats:sup>1<\/jats:sup>\n                    <\/jats:xref>\n                  <\/jats:p>","DOI":"10.1145\/3732282","type":"journal-article","created":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T06:53:55Z","timestamp":1745477635000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["Data Augmentation on Graphs: A Technical Survey"],"prefix":"10.1145","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5062-4183","authenticated-orcid":false,"given":"Jiajun","family":"Zhou","sequence":"first","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China and Binjiang Institute of Artificial Intelligence, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4474-615X","authenticated-orcid":false,"given":"Chenxuan","family":"Xie","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7482-6524","authenticated-orcid":false,"given":"Shengbo","family":"Gong","sequence":"additional","affiliation":[{"name":"Emory University, Atlanta, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2914-912X","authenticated-orcid":false,"given":"Zhenyu","family":"Wen","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2926-4416","authenticated-orcid":false,"given":"Xiangyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"City University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1042-470X","authenticated-orcid":false,"given":"Qi","family":"Xuan","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China and Binjiang Institute of Artificial Intelligence, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3117-2211","authenticated-orcid":false,"given":"Xiaoniu","family":"Yang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Electromagnetic Space Security, Jiaxing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,6,14]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2022.100527"},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","first-page":"109631","DOI":"10.1016\/j.knosys.2022.109631","article-title":"Graph barlow twins: A self-supervised representation learning framework for graphs","author":"Bielak Piotr","year":"2022","unstructured":"Piotr Bielak, Tomasz Kajdanowicz, and Nitesh V. 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Applied Intelligence 54, 5 (2024), 4411\u20134424.","journal-title":"Applied Intelligence"},{"key":"e_1_3_2_59_2","first-page":"14175","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Lu Weigang","year":"2024","unstructured":"Weigang Lu, Ziyu Guan, Wei Zhao, Yaming Yang, and Long Jin. 2024. Nodemixup: Tackling under-reaching for graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence. 14175\u201314183."},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441734"},{"key":"e_1_3_2_61_2","article-title":"Fused gromov-wasserstein graph mixup for graph-level classifications","volume":"36","author":"Ma Xinyu","year":"2024","unstructured":"Xinyu Ma, Xu Chu, Yasha Wang, Yang Lin, Junfeng Zhao, Liantao Ma, and Wenwu Zhu. 2024. Fused gromov-wasserstein graph mixup for graph-level classifications. Advances in Neural Information Processing Systems 36 (2024), 15252\u201315276.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_62_2","unstructured":"Maria Marrium and Arif Mahmood. 2022. Data augmentation for graph data: Recent advancements. arXiv:2208.11973. Retrieved from https:\/\/arxiv.org\/abs\/2208.11973"},{"issue":"8","key":"e_1_3_2_63_2","doi-asserted-by":"crossref","first-page":"2529","DOI":"10.1021\/jm1013693","article-title":"Synopsis of some recent tactical application of bioisosteres in drug design","volume":"54","author":"Meanwell Nicholas A.","year":"2011","unstructured":"Nicholas A. Meanwell. 2011. Synopsis of some recent tactical application of bioisosteres in drug design. 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