让图神经网络在分布外测试时更稳定，通过优化隐藏表示而非参数。
Beyond Parameter Finetuning: Test-Time Representation Refinement for Node Classification
- 测试时直接调整节点隐含表示，避免参数微调的灾难性遗忘。
- 在五个基准数据集上表现优于现有方法，提升显著且稳定。
- 适合部署在真实场景中需要持续适应的新节点任务。
图神经网络在分布外（OOD）测试场景中常出现性能下降。尽管测试时训练（TTT）提供了有前景的解决方案，但现有的参数微调（PaFT）方法存在灾难性遗忘问题，限制了其实际应用。本文提出一种新型测试时表示微调框架TTReFT，将适应目标从模型参数转向隐含表示。具体通过三项创新实现：(1) 基于不确定性的节点选择机制，精准定位需干预的节点；(2) 低秩表示干预策略，保留预训练知识；(3) 适配干预策略的动态掩码自编码器，自动调整掩码方式。理论上，我们为TTReFT在OOD设置下建立了性能保证。实验结果表明，在五个基准数据集上的广泛验证中，TTReFT始终表现出一致且优越的性能。本工作确立了表示微调作为图神经网络测试时训练的新范式，兼具理论基础与实际应用价值。
原文摘要 · Abstract (English)
Graph Neural Networks frequently exhibit significant performance degradation in the out-of-distribution test scenario. While test-time training (TTT) offers a promising solution, existing Parameter Finetuning (PaFT) paradigm suffer from catastrophic forgetting, hindering their real-world applicability. We propose TTReFT, a novel Test-Time Representation FineTuning framework that transitions the adaptation target from model parameters to latent representations. Specifically, TTReFT achieves this through three key innovations: (1) uncertainty-guided node selection for specific interventions, (2) low-rank representation interventions that preserve pre-trained knowledge, and (3) an intervention-aware masked autoencoder that dynamically adjust masking strategy to accommodate the node selection scheme. Theoretically, we establish guarantees for TTReFT in OOD settings. Empirically, extensive experiments across five benchmark datasets demonstrate that TTReFT achieves consistent and superior performance. Our work establishes representation finetuning as a new paradigm for graph TTT, offering both theoretical grounding and immediate practical utility for real-world deployment.
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