Spatio-temporal graph neural networks have demonstrated efficacy in capturing complex dependencies for urban computing tasks such as forecasting and kriging. However, their performance is constrained by the reliance on extensive data for training on specific tasks, which limits their adaptability to new urban domains with varied demands. Although transfer learning has been proposed to address this problem by leveraging knowledge across domains, cross-task generalization remains underexplored in spatio-temporal graph transfer learning methods due to the absence of a unified framework. To bridge this gap, we propose Spatio-Temporal Graph Prompting (STGP), a prompt-enhanced transfer learning framework capable of adapting to diverse tasks in data-scarce domains. Specifically, we first unify different tasks into a single template and introduce a task-agnostic network architecture that aligns with this template. This approach enables the capture of spatio-temporal dependencies shared across tasks. Furthermore, we employ learnable prompts to achieve domain and task transfer in a two-stage prompting pipeline, enabling the prompts to effectively capture domain knowledge and task-specific properties at each stage. Extensive experiments demonstrate that STGP outperforms state-of-the-art baselines in three downstream tasks forecasting, kriging, and extrapolation by a notable margin.

翻译：时空图神经网络在城市计算任务（如预测与克里金插值）中展现出捕获复杂依赖关系的有效性。然而，其性能受限于对特定任务需依赖大量数据进行训练，这限制了其对具有多样化需求的新城市领域的适应性。尽管已有研究通过跨领域知识迁移来应对该问题，但由于缺乏统一框架，时空图迁移学习方法中的跨任务泛化能力仍未被充分探索。为填补这一空白，我们提出时空图提示学习（STGP）——一种提示增强的迁移学习框架，可适配数据稀缺领域中的多样化任务。具体而言，我们首先将不同任务统一至单一模板，并引入与该模板对齐的任务无关网络架构。该方法能够捕获跨任务共享的时空依赖关系。此外，我们在两阶段提示流水线中采用可学习提示实现领域与任务迁移，使各阶段提示能有效捕获领域知识与任务特定属性。大量实验表明，STGP在预测、克里金插值与外推三项下游任务中显著超越现有最优基线方法。