Test-Time Adaptation for Graph Learning: A Systematic Survey

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.