Spatiotemporal learning, which aims at extracting spatiotemporal correlations from the collected spatiotemporal data, is a research hotspot in recent years. And considering the inherent graph structure of spatiotemporal data, recent works focus on capturing spatial dependencies by utilizing Graph Convolutional Networks (GCNs) to aggregate vertex features with the guidance of adjacency matrices. In this paper, with extensive and deep-going experiments, we comprehensively analyze existing spatiotemporal graph learning models and reveal that extracting adjacency matrices with carefully design strategies, which are viewed as the key of enhancing performance on graph learning, are largely ineffective. Meanwhile, based on these experiments, we also discover that the aggregation itself is more important than the way that how vertices are aggregated. With these preliminary, a novel efficient Graph-Free Spatial (GFS) learning module based on layer normalization for capturing spatial correlations in spatiotemporal graph learning. The proposed GFS module can be easily plugged into existing models for replacing all graph convolution components. Rigorous theoretical proof demonstrates that the time complexity of GFS is significantly better than that of graph convolution operation. Extensive experiments verify the superiority of GFS in both the perspectives of efficiency and learning effect in processing graph-structured data especially extreme large scale graph data.

翻译：时空学习旨在从收集的时空数据中提取时空相关性，是近年来的研究热点。考虑到时空数据固有的图结构，近期研究聚焦于利用图卷积网络（GCN）在邻接矩阵引导下聚合顶点特征以捕获空间依赖性。本文通过广泛且深入的实验，全面分析了现有时空图学习模型，发现精心设计的邻接矩阵提取策略——通常被视为提升图学习性能的关键——在很大程度上是无效的。同时，基于这些实验，我们还发现聚合操作本身比顶点聚合方式更为重要。基于此，我们提出了一种基于层归一化的高效无图空间（GFS）学习模块，用于捕获时空图学习中的空间相关性。所提出的GFS模块可轻松嵌入现有模型以替代所有图卷积组件。严格的理论证明表明，GFS的时间复杂度显著优于图卷积运算。大量实验验证了GFS在处理图结构数据（尤其是超大规模图数据）时，在效率与学习效果两个维度均具有优越性。