COIN-GNN: Inductive Spatial-Temporal Prediction for Continuous Distribution Shifts via Graph Neural Networks

Distribution shifts from external events and new entities can significantly compromise spatial-temporal prediction accuracy, potentially leading to severe outcomes like traffic accidents. Existing methods often fail under these conditions due to two main limitations: they focus on invariant patterns, missing the diversity required to capture the evolving dynamics of distribution shifts; they rely on often inaccessible future knowledge, such as spatial information of new entities, limiting their generalizability. To address these limitations, we formally define the problem of inductive spatial-temporal prediction under continuous distribution shifts and introduce the Contrastive Learning Based Inductive Graph Neural Network (COIN-GNN) as a solution. We develop a novel metric, Relation Importance (RI), to effectively select stable entities and distinct spatial relationships, forming an informative subgraph. Additionally, we construct an informative temporal memory buffer to store and review influential timestamps identified using influence functions. COIN-GNN then generates pseudo-observations for unstable and uninformative entities during these influential timestamps, simulating potential distribution shifts. By applying contrastive learning, the network learns stable and informative representations that can effectively counter distribution shifts without relying on future knowledge. Our extensive experiments on several real-world datasets-from traffic to weather-demonstrate COIN-GNN's superior performance across different domains without requiring future knowledge.