{"type":"ScholarlyArticle","title":"Test-Time Adaptation for Graph Learning: A Systematic Survey","authors":[{"name":"Jiayi Chen","slug":"jiayi-chen","given":"Jiayi","family":"Chen","affiliation":""},{"name":"Xin Zheng","slug":"xin-zheng","given":"Xin","family":"Zheng","affiliation":""},{"name":"Bo Li","slug":"bo-li","given":"Bo","family":"Li","affiliation":""},{"name":"Zeyu Wang","slug":"zeyu-wang","given":"Zeyu","family":"Wang","affiliation":""},{"name":"Yanqing Guo","slug":"yanqing-guo","given":"Yanqing","family":"Guo","affiliation":""},{"name":"Feng Xia","slug":"feng-xia","given":"Feng","family":"Xia","affiliation":""}],"abstract":"Graph distribution shifts between training and test graphs pose severe challenges to the generalization of graph neural networks (GNNs). In real-world deployment, application environments are continuously evolving, while retraining or redesigning GNNs is often costly and impractical. In light of this, test-time adaptation on graphs, which aims to dynamically adapt well-trained GNNs or adjust test graphs to improve inference performance, has attracted growing attention as a practical solution. In this survey, we provide a comprehensive review of test-time adaptation on graphs, an emerging yet underexplored research direction. We identify two fundamental challenges: (1) Data-level: complex graph distribution shifts; and (2) Model-level: limited test-time learning information. Upon this, we present a systematic taxonomy of existing methods into (a) model-centric, (b) data-centric, and (c) hybrid methods, followed by a summary of representative applications, benchmarks, and open opportunities. We aim to bridge the gap between laboratory GNN development and real-world deployment via test-time adaptation. The repository of papers, code, and datasets is at https://github.com/jiayichen1121/Test-Time-Adaptation-for-Graph-Learning.","doi":"10.24963/ijcai.2026/866","url":"https://ijcai.org/proceedings/2026/866","pdf":"https://ijcai.org/proceedings/2026/0866.pdf","markdown":"https://ijcai.org/proceedings/2026/866.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/866","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Survey Track","track":"Survey Track","number":866,"pages":{"first":7798,"last":7806},"keywords":["Data Mining: Networks","Data Mining: Mining graphs"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}