PlugSI让传感器插值模型在测试时自动适应新图结构，提升精度。
PlugSI: Plug-and-Play Test-Time Graph Adaptation for Spatial Interpolation
- 通过动态适配未知拓扑结构，实现测试时图结构自适应
- 引入时间平衡机制，防止噪声导致的预测漂移
- 可无缝接入现有方法，显著降低10.81%的平均绝对误差
随着物联网和边缘计算的快速发展，传感器网络已成为不可或缺的技术，推动了大规模部署的需求。然而，高昂的部署成本制约了其扩展性。空间插值（Spatial Interpolation, SI）通过引入虚拟传感器，利用图结构从观测传感器推断读数，缓解此问题。但现有基于图的SI方法依赖预训练模型，无法在测试时适应更大或未见的图结构，且未充分利用测试数据。为此，我们提出PlugSI，一个即插即用的测试时图结构自适应框架，包含两项关键创新：首先，设计未知拓扑适配器（UTA），在测试时对每个小批次的新图结构进行动态适配，提升预训练模型的泛化能力；其次，引入时间平衡适配器（TBA），维持稳定的历史共识以指导UTA，防止当前批次噪声引发的预测漂移。实验表明，PlugSI可无缝集成至现有基于图的SI方法中，在多个数据集上实现显著性能提升（如MAE降低10.81%）。
原文摘要 · Abstract (English)
With the rapid advancement of IoT and edge computing, sensor networks have become indispensable, driving the need for large-scale sensor deployment. However, the high deployment cost hinders their scalability. To tackle the issues, Spatial Interpolation (SI) introduces virtual sensors to infer readings from observed sensors, leveraging graph structure. However, current graph-based SI methods rely on pre-trained models, lack adaptation to larger and unseen graphs at test-time, and overlook test data utilization. To address these issues, we propose PlugSI, a plug-and-play framework that refines test-time graph through two key innovations. First, we design an Unknown Topology Adapter (UTA) that adapts to the new graph structure of each small-batch at test-time, enhancing the generalization of SI pre-trained models. Second, we introduce a Temporal Balance Adapter (TBA) that maintains a stable historical consensus to guide UTA adaptation and prevent drifting caused by noise in the current batch. Empirically, extensive experiments demonstrate PlugSI can be seamlessly integrated into existing graph-based SI methods and provide significant improvement (e.g., a 10.81% reduction in MAE).
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