WaveGuard: Coordinating Message Passing and Test-Time Adaptation with Spectral Memory

Graph test-time adaptation (GTTA) updates a pretrained graph neural network using an unlabeled target graph without revisiting the source graph. Under structural shift, altered neighborhoods can both degrade node representations and corrupt the signals used for adaptation. Predictive confidence alone does not distinguish these effects, particularly when a model is confidently wrong. We propose WaveGuard, which uses a fixed node-wise intervention score to coordinate message passing and unsupervised updates. A compact class–degree-conditioned memory of source graph-wavelet energy provides a reference for target spectral deviation. Combining this deviation with frozen-model uncertainty yields a score that controls direction-aware soft topology purification and calibrates entropy, anchor, and consistency losses. The score, reweighted graph, and prediction anchors remain fixed during adaptation, while deployment retains only aggregate source statistics. Experiments on five graph benchmarks covering 23 target scenarios demonstrate WaveGuard’s effectiveness under structural, temporal, and mixed shifts. WaveGuard ranks first in six of eight CSBM scenarios, achieves the highest average accuracy on ogbn-arxiv, and outperforms the evaluated baselines in all three PubMed settings and two of three Twitch transfer tasks. On Elliptic, it surpasses the strongest baseline by 6.12 and 6.63 percentage points in Balanced Accuracy and Macro-F1, respectively. Edge-weight analysis and ablation studies demonstrate the complementary contributions of message control and objective calibration.