Node-Wise Adaptive Scale Fusion: A Dual-View Framework for Spatio-Temporal Graphs

Multi-scale graph neural networks have shown strong performance in multivariate time-series forecasting by integrating representations at different temporal resolutions. However, existing fusion mechanisms typically assign the same scale weights to all nodes, implicitly assuming spatially homogeneous temporal preferences. This assumption is restrictive for real-world graph-structured systems, where different nodes may exhibit substantially different dynamics and noise characteristics. To address this limitation, we propose Node-Wise Adaptive Scale Fusion (NAF), a lightweight plug-in module that learns node-specific fusion weights through two complementary views. The adaptive propagation (AP) branch balances node-local evidence with graph-shared information, while the self-attention aggregation (SAAG) branch evaluates the reliability of each scale according to its consistency with a robust full-window trend representation. We further show that globally shared fusion is optimal only under homogeneous or proportionally aligned node reliability profiles, whereas node-wise adaptation yields a strict advantage when such profiles become heterogeneous; the proposed similarity gate also preserves the ordering of branch signal-to-noise ratios under mild conditions. Experiments on four multivariate time-series benchmarks demonstrate that NAF-GNN achieves the best performance in most forecasting settings, with particularly clear gains on heterogeneous traffic data and under additive input corruption. Moreover, the fusion overhead of NAF is independent of the number of graph nodes.