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.

我们提出了一个简单的多模式时空数据建模框架，通过设计一个跨模式空间关系学习组件来自适应地建立多个模式之间的连接，并沿着学到的连接传播信息。同时，我们使用多层感知机来捕捉时态依赖和通道相关性，实验证明这一模型在三个真实数据集上始终优于基准模型，具有更低的空间和时间复杂度，为时空数据建模开辟了一种有前景的方向。同时，我们也验证了跨模式空间关系学习模块的一般化能力。