{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T21:58:15Z","timestamp":1778709495238,"version":"3.51.4"},"reference-count":195,"publisher":"Association for Computing Machinery (ACM)","issue":"2","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>\n                    Distribution shifts on graphs\u2014the discrepancies in data distribution between training and employing a graph machine learning model\u2014are ubiquitous and often unavoidable in real-world applications. These shifts may severely deteriorate model performance, posing significant challenges for reliable graph machine learning. In recent years, there has been a surge in research on graph machine learning specifically designed to tackle such distribution shifts, aiming to train models to achieve satisfactory performance on Out-of-Distribution (OOD) test data. This survey provides an up-to-date and forward-looking review of deep graph learning under distribution shifts. We categorize the field into three primary scenarios: graph OOD generalization, training-time graph OOD adaptation, and test-time graph OOD adaptation. We begin by formally formulating the problems and discussing various types of distribution shifts that can affect graph learning, such as covariate shifts and concept shifts. To provide a structured understanding of the literature, we introduce a systematic taxonomy that classifies existing methods into model-centric and data-centric approaches, investigating the techniques used in each category. We also summarize commonly used datasets in this research area to facilitate further investigation. Finally, we point out promising research directions and the corresponding challenges to encourage further study in this vital domain. Additionally, we provide a continuously updated reading list at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/kaize0409\/Awesome-Graph-OOD\">https:\/\/github.com\/kaize0409\/Awesome-Graph-OOD<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3785475","type":"journal-article","created":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T14:15:39Z","timestamp":1766153739000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["A Survey of Deep Graph Learning under Distribution Shifts: From Graph Out-of-Distribution Generalization to Adaptation"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2678-8556","authenticated-orcid":false,"given":"Kexin","family":"Zhang","sequence":"first","affiliation":[{"name":"Northwestern University, Evanston, Illinois, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5774-8672","authenticated-orcid":false,"given":"Shuhan","family":"Liu","sequence":"additional","affiliation":[{"name":"Northwestern University, Evanston, Illinois, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1273-7694","authenticated-orcid":false,"given":"Song","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9907-2009","authenticated-orcid":false,"given":"Weili","family":"Shi","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7099-7905","authenticated-orcid":false,"given":"Chen","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3742-0845","authenticated-orcid":false,"given":"Pan","family":"Li","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, Georgia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1205-8632","authenticated-orcid":false,"given":"Sheng","family":"Li","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1878-817X","authenticated-orcid":false,"given":"Jundong","family":"Li","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6684-6752","authenticated-orcid":false,"given":"Kaize","family":"Ding","sequence":"additional","affiliation":[{"name":"Northwestern University, Evanston, Illinois, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,2,5]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"145","volume-title":"International Conference on Machine Learning","author":"Ahuja Kartik","year":"2020","unstructured":"Kartik Ahuja, Karthikeyan Shanmugam, Kush Varshney, and Amit Dhurandhar. 2020. 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