WavCIL: Wavelet Coefficient-Domain Invariant Learning for Dynamic Graph OOD Generalization

Dynamic Graph Neural Networks (DyGNNs) demonstrate powerful representation capabilities in crucial time-series systems by leveraging graph structure and temporal dynamics. However, existing dynamic graph models often suffer from unpredictable distribution shifts induced by complex time-varying factors such as emergency incidents, leading to severe performance degradation due to their low out-of-distribution (OOD) generalization. Graph invariant learning has been extensively studied to solve this problem through feature disentanglement in the time or spectral domain. Despite recent advances, existing methods suffer from critical limitations in practice: Temporal-based methods struggle to disentangle transient perturbations and stable patterns that are highly overlapping and entangled within the same period. While spectral-based methods attempt spectral-domain disentanglement via the global Fourier transform, these methods inherently lose temporal localization and fail to effectively characterize distribution shifts driven by time-varying factors. To this end, in this paper, we propose Wavelet Coefficient-Domain Invariant Learning (WavCIL), a novel framework for resolving distribution shifts by conducting disentanglement within a wavelet coefficient domain endowed with time-frequency localization capabilities. Specifically, WavCIL consists of two key components: (i) a wavelet transform module, which leverages a set of wavelet bases to map the input temporal signals into wavelet coefficient domain, simultaneously capturing frequency components and localizing their temporal occurrences; and (ii) a coefficient domain invariant learning module, which utilizes an adaptive mask mechanism to disentangle stable causal patterns invariant to time-varying factors from spurious patterns driven by time-varying factors in the coefficient domain, guiding the prediction process to rely more on the causal patterns. Extensive experiments on several benchmark datasets demonstrate that WavCIL achieves state-of-the-art generalization performance when handling distribution shifts. Our code is released at https://anonymous.4open.science/r/WavCIL-4B8C.