National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China.
School of Computer Science and Technology, Beijing Institute of Technology, 100081, China.
Neural Netw. 2022 Oct;154:190-202. doi: 10.1016/j.neunet.2022.07.017. Epub 2022 Jul 16.
Spatial-temporal graph modeling has been widely studied in many fields, such as traffic forecasting and energy analysis, where data has time and space properties. Existing methods focus on capturing stable and dynamic spatial correlations by constructing physical and virtual graphs along with graph convolution and temporal modeling. However, existing methods tending to smooth node features may obscure the spatial-temporal patterns among nodes. Worse, the graph structure is not always available in some fields, while the manually constructed stable or dynamic graphs cannot necessarily reflect the true spatial correlations either. This paper proposes a Subgraph-Aware Graph Structure Revision network (SAGSR) to overcome these limitations. Architecturally, a subgraph-aware structure revision graph convolution module (SASR-GCM) is designed, which revises the learned stable graph to obtain a dynamic one to automatically infer the dynamics of spatial correlations. Each of these two graphs is separated into one homophilic subgraph and one heterophilic subgraph by a subgraph-aware graph convolution mechanism, which aggregates similar nodes in the homophilic subgraph with positive weights, while keeping nodes with dissimilar features in the heterophilic subgraph mutually away with negative aggregation weights to avoid pattern obfuscation. By combining a gated multi-scale temporal convolution module (GMS-TCM) for temporal modeling, SAGSR can efficiently capture the spatial-temporal correlations and extract complex spatial-temporal graph features. Extensive experiments, conducted on two specific tasks: traffic flow forecasting and energy consumption forecasting, indicate the effectiveness and superiority of our proposed approach over several competitive baselines.
时空图建模在交通预测和能源分析等领域得到了广泛研究，这些领域的数据具有时间和空间属性。现有的方法侧重于通过构建物理和虚拟图以及图卷积和时间建模来捕捉稳定和动态的空间相关性。然而，现有的方法往往会平滑节点特征，从而掩盖节点之间的时空模式。更糟糕的是，在某些领域中，图结构并不总是可用的，而手动构建的稳定或动态图也不一定能反映真实的空间相关性。本文提出了一种子图感知图结构修正网络（SAGSR）来克服这些限制。在架构上，设计了一个子图感知结构修正图卷积模块（SASR-GCM），它修正学习到的稳定图以获得动态图，从而自动推断空间相关性的动态性。这两个图中的每一个都通过一个子图感知图卷积机制分为一个同配子图和一个异配子图，同配子图中的相似节点用正权重聚集，而异配子图中的不相似特征节点用负聚合权重相互远离，以避免模式混淆。通过结合门控多尺度时间卷积模块（GMS-TCM）进行时间建模，SAGSR 可以有效地捕捉时空相关性并提取复杂的时空图特征。在两个特定任务上的广泛实验，即交通流量预测和能源消耗预测，表明了我们提出的方法优于几个竞争基线的有效性和优越性。
