Mining Spatio-Temporal Relations via Self-Paced Graph Contrastive Learning

Modeling complex spatial and temporal dependencies are indispensable for location-bound time series learning. Existing methods, typically relying on graph neural networks (GNNs) and temporal learning modules based on recurrent neural networks, have achieved significant performance improvements. However, their representation capabilities and prediction results are limited when pre-defined graphs are unavailable. Unlike spatio-temporal GNNs focusing on designing complex architectures, we propose a novel adaptive graph construction strategy: Self-Paced Graph Contrastive Learning (SPGCL). It learns informative relations by maximizing the distinguishing margin between positive and negative neighbors and generates an optimal graph with a self-paced strategy. Specifically, the existing neighborhoods iteratively absorb more reliable nodes with the highest affinity scores as new neighbors to generate the next-round neighborhoods, and augmentations are applied to improve the transferability and robustness. As the adaptively self-paced graph approaches the optimized graph for prediction, the mutual information between nodes and the corresponding neighbors is maximized. Our work provides a new perspective of addressing spatio-temporal learning problems beyond information aggregation in Euclidean space and can be generalized to different tasks. Extensive experiments conducted on two typical spatio-temporal learning tasks (traffic forecasting and land displacement prediction) demonstrate the superior performance of SPGCL against the state-of-the-art.