Spatial-temporal graphs are widely used in a variety of real-world applications. Spatial-Temporal Graph Neural Networks (STGNNs) have emerged as a powerful tool to extract meaningful insights from this data. However, in real-world applications, most nodes may not possess any available temporal data during training. For example, the pandemic dynamics of most cities on a geographical graph may not be available due to the asynchronous nature of outbreaks. Such a phenomenon disagrees with the training requirements of most existing spatial-temporal forecasting methods, which jeopardizes their effectiveness and thus blocks broader deployment. In this paper, we propose to formulate a novel problem of inductive forecasting with limited training data. In particular, given a spatial-temporal graph, we aim to learn a spatial-temporal forecasting model that can be easily generalized onto those nodes without any available temporal training data. To handle this problem, we propose a principled framework named ST-FiT. ST-FiT consists of two key learning components: temporal data augmentation and spatial graph topology learning. With such a design, ST-FiT can be used on top of any existing STGNNs to achieve superior performance on the nodes without training data. Extensive experiments verify the effectiveness of ST-FiT in multiple key perspectives.

翻译：时空图在众多现实应用中广泛使用。时空图神经网络已成为从这类数据中提取有意义洞察的强大工具。然而，在实际应用中，大多数节点在训练期间可能不具备任何可用的时序数据。例如，由于疫情暴发的异步性，地理图上大多数城市的疫情动态数据可能无法获取。这种现象与现有大多数时空预测方法的训练要求相悖，损害了其有效性，从而阻碍了更广泛的部署。本文提出构建一个新颖的有限训练数据下的归纳式预测问题。具体而言，给定一个时空图，我们的目标是学习一个能够轻松泛化到那些没有任何可用时序训练数据的节点上的时空预测模型。为解决此问题，我们提出了一个名为ST-FiT的原则性框架。ST-FiT包含两个关键学习组件：时序数据增强与空间图拓扑学习。通过这种设计，ST-FiT可在任何现有时空图神经网络之上使用，从而在无训练数据的节点上实现卓越性能。大量实验从多个关键角度验证了ST-FiT的有效性。