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In addition, real-world graphs usually possess complex structural information and features. Therefore, to improve the applicability of GNNs and fully encode the complicated topological information, Knowledge Distillation on Graphs (KDG) has been introduced to build a smaller but effective model, leading to model compression and performance improvement. Recently, KDG has achieved considerable progress, with many studies proposed. In this survey, we systematically review these works. Specifically, we first introduce the challenges and bases of KDG, then categorize and summarize the existing work of KDG by answering the following three questions: (1) what to distillate, (2) who to whom, and (3) how to distillate. We offer in-depth comparisons and elucidate the strengths and weaknesses of each design. Finally, we share our thoughts on future research directions.<\/jats:p>","DOI":"10.1145\/3711121","type":"journal-article","created":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T11:04:52Z","timestamp":1738235092000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":35,"title":["Knowledge Distillation on Graphs: A Survey"],"prefix":"10.1145","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2795-6080","authenticated-orcid":false,"given":"Yijun","family":"Tian","sequence":"first","affiliation":[{"name":"University of Notre Dame, Notre Dame, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0802-1506","authenticated-orcid":false,"given":"Shichao","family":"Pei","sequence":"additional","affiliation":[{"name":"University of Massachusetts Boston, Boston, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3574-5665","authenticated-orcid":false,"given":"Xiangliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Notre Dame, Notre Dame, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8349-7926","authenticated-orcid":false,"given":"Chuxu","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Connecticut, Storrs, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3932-5956","authenticated-orcid":false,"given":"Nitesh V.","family":"Chawla","sequence":"additional","affiliation":[{"name":"University of Notre Dame, Notre Dame, United States"}]}],"member":"320","published-online":{"date-parts":[[2025,3,5]]},"reference":[{"key":"e_1_3_1_2_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Allen-Zhu Zeyuan","year":"2020","unstructured":"Zeyuan Allen-Zhu and Yuanzhi Li. 2020. 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