Temporal Memory-Aware Online Test-Time Adaptation on Dynamic Graphs

2026-08-28

AI Summary

本文提出DGOTTA框架，通过时间记忆感知的在线测试时适应方法解决动态图上分布偏移导致的模型泛化问题。

Abstract

Test-time adaptation (TTA) on graphs aims to adapt a graph neural network (GNN) that is well-trained on the training graph to the test graph, which involves potential distribution shifts that may harm model generalization and test-time inference. While recent efforts have investigated TTA on static graphs, there is still a research gap on dynamic graphs learned with dynamic GNN (DGNN) models, where both structural connectivity and node semantics evolve continuously over time. This makes adapting a DGNN model for reliable test-time performance substantially challenging. To fill this gap, in this work, we propose a novel framework of temporal memory-aware Online Test-Time Adaptation on Dynamic Graphs, named DGOTTA, to effectively adapt well-trained DGNNs during test time. Specifically, the proposed DGOTTA contains three modules: (1) temporal-aware augmentation, to extend the diversity of test dynamic graphs for addressing complex temporal and spatial shifts; (2) memory-aware model prediction, to alleviate catastrophic forgetting; (3) consistency-guided online adaptation, to enforce temporal alignment and memory smoothness. Extensive experiments on three real-world datasets and four DGNN backbones demonstrate that DGOTTA significantly improves generalization under diverse distribution shifts and multiple model architectures.

Problem

• Test-time Adaptation
• Dynamic Graphs
• Graph Neural Network
• Temporal Shifts
• Model Generalization

Innovation

• temporal-aware augmentation
• memory-aware model prediction
• consistency-guided online adaptation