  Spatio-temporal prediction is crucial in numerous real-world applications,
including traffic forecasting and crime prediction, which aim to improve public
transportation and safety management. Many state-of-the-art models demonstrate
the strong capability of spatio-temporal graph neural networks (STGNN) to
capture complex spatio-temporal correlations. However, despite their
effectiveness, existing approaches do not adequately address several key
challenges. Data quality issues, such as data scarcity and sparsity, lead to
data noise and a lack of supervised signals, which significantly limit the
performance of STGNN. Although recent STGNN models with contrastive learning
aim to address these challenges, most of them use pre-defined augmentation
strategies that heavily depend on manual design and cannot be customized for
different Spatio-Temporal Graph (STG) scenarios. To tackle these challenges, we
propose a new spatio-temporal contrastive learning (CL4ST) framework to encode
robust and generalizable STG representations via the STG augmentation paradigm.
Specifically, we design the meta view generator to automatically construct node
and edge augmentation views for each disentangled spatial and temporal graph in
a data-driven manner. The meta view generator employs meta networks with
parameterized generative model to customize the augmentations for each input.
This personalizes the augmentation strategies for every STG and endows the
learning framework with spatio-temporal-aware information. Additionally, we
integrate a unified spatio-temporal graph attention network with the proposed
meta view generator and two-branch graph contrastive learning paradigms.
Extensive experiments demonstrate that our CL4ST significantly improves
performance over various state-of-the-art baselines in traffic and crime
prediction.
