Among various region embedding methods, graph-based region relation learning models stand out, owing to their strong structure representation ability for encoding spatial correlations with graph neural networks. Despite their effectiveness, several key challenges have not been well addressed in existing methods: i) Data noise and missing are ubiquitous in many spatio-temporal scenarios due to a variety of factors. ii) Input spatio-temporal data (e.g., mobility traces) usually exhibits distribution heterogeneity across space and time. In such cases, current methods are vulnerable to the quality of the generated region graphs, which may lead to suboptimal performance. In this paper, we tackle the above challenges by exploring the Automated Spatio-Temporal graph contrastive learning paradigm (AutoST) over the heterogeneous region graph generated from multi-view data sources. Our \model\ framework is built upon a heterogeneous graph neural architecture to capture the multi-view region dependencies with respect to POI semantics, mobility flow patterns and geographical positions. To improve the robustness of our GNN encoder against data noise and distribution issues, we design an automated spatio-temporal augmentation scheme with a parameterized contrastive view generator. AutoST can adapt to the spatio-temporal heterogeneous graph with multi-view semantics well preserved. Extensive experiments for three downstream spatio-temporal mining tasks on several real-world datasets demonstrate the significant performance gain achieved by our \model\ over a variety of baselines. The code is publicly available at https://github.com/HKUDS/AutoST.

翻译：在各种区域嵌入方法中，基于图的区域关系学习模型因其强大的结构表示能力（通过图神经网络编码空间相关性）而脱颖而出。然而，尽管现有方法有效，但仍存在几个关键挑战：i) 由于多种因素，数据噪声和缺失在众多时空场景中普遍存在。ii) 输入的时空数据（例如，移动轨迹）通常表现出跨空间和时间的分布异质性。在这种情况下，当前方法易受生成区域图质量的影响，可能导致性能次优。本文通过探索基于多视图数据源生成的异质区域图上的自动时空图对比学习范式（AutoST）来应对上述挑战。我们的\model\框架基于异质图神经架构，以捕捉关于POI语义、移动流模式和地理位置的多元区域依赖关系。为了提高GNN编码器对数据噪声和分布问题的鲁棒性，我们设计了一种自动化时空增强方案，配备参数化的对比视图生成器。AutoST能够适应时空异质图，同时很好地保留多视图语义。在多个真实世界数据集上针对三个下游时空挖掘任务的广泛实验表明，我们的\model\相比各种基线取得了显著的性能提升。代码公开发布于 https://github.com/HKUDS/AutoST。