Spatial-temporal data modeling aims to mine the underlying spatial relationships and temporal dependencies of objects in a system. However, most existing methods focus on the modeling of spatial-temporal data in a single mode, lacking the understanding of multiple modes. Though very few methods have been presented to learn the multi-mode relationships recently, they are built on complicated components with higher model complexities. In this paper, we propose a simple framework for multi-mode spatial-temporal data modeling to bring both effectiveness and efficiency together. Specifically, we design a general cross-mode spatial relationships learning component to adaptively establish connections between multiple modes and propagate information along the learned connections. Moreover, we employ multi-layer perceptrons to capture the temporal dependencies and channel correlations, which are conceptually and technically succinct. Experiments on three real-world datasets show that our model can consistently outperform the baselines with lower space and time complexity, opening up a promising direction for modeling spatial-temporal data. The generalizability of the cross-mode spatial relationships learning module is also validated.

翻译：时空数据建模旨在挖掘系统中对象的潜在空间关系与时间依赖关系。然而，现有方法多聚焦于单模态时空数据建模，缺乏对多模态数据的理解能力。尽管近期有极少数方法尝试学习多模态关系，但这些方法基于复杂组件构建，模型复杂度较高。本文提出一种面向多模态时空数据建模的简洁框架，兼顾效果与效率。具体而言，我们设计了一种通用的跨模态空间关系学习组件，可自适应建立多模态间的连接，并沿所学连接传播信息。此外，我们采用多层感知机捕获时间依赖性与通道相关性，该方法在概念与技术层面均具简洁性。在三个真实世界数据集上的实验表明，本模型能以更低的空间与时间复杂度持续超越基线方法，为时空数据建模开辟了有前景的新方向。研究同时验证了跨模态空间关系学习模块的泛化能力。