Spatio-temporal kriging is an important problem in web and social applications, such as Web or Internet of Things, where things (e.g., sensors) connected into a web often come with spatial and temporal properties. It aims to infer knowledge for (the things at) unobserved locations using the data from (the things at) observed locations during a given time period of interest. This problem essentially requires \emph{inductive learning}. Once trained, the model should be able to perform kriging for different locations including newly given ones, without retraining. However, it is challenging to perform accurate kriging results because of the heterogeneous spatial relations and diverse temporal patterns. In this paper, we propose a novel inductive graph representation learning model for spatio-temporal kriging. We first encode heterogeneous spatial relations between the unobserved and observed locations by their spatial proximity, functional similarity, and transition probability. Based on each relation, we accurately aggregate the information of most correlated observed locations to produce inductive representations for the unobserved locations, by jointly modeling their similarities and differences. Then, we design relation-aware gated recurrent unit (GRU) networks to adaptively capture the temporal correlations in the generated sequence representations for each relation. Finally, we propose a multi-relation attention mechanism to dynamically fuse the complex spatio-temporal information at different time steps from multiple relations to compute the kriging output. Experimental results on three real-world datasets show that our proposed model outperforms state-of-the-art methods consistently, and the advantage is more significant when there are fewer observed locations. Our code is available at https://github.com/zhengchuanpan/INCREASE.

翻译：时空克里金预测是网络与社交应用（如物联网）中的一个重要问题，其中联网物体（如传感器）通常具有空间和时间属性。其目标是在给定时间区间内，利用观测位置的数据推断未观测位置的知识。该问题本质上要求归纳学习能力：模型训练后无需重新训练即可对不同位置（包括新出现的位置）执行克里金预测。然而，由于异构的空间关系和多样化的时间模式，实现准确预测具有挑战性。本文提出一种新颖的归纳式图表示学习模型用于时空克里金预测。我们首先通过空间邻近性、功能相似性和转移概率编码未观测位置与观测位置之间的异构空间关系。基于每种关系，通过联合建模其相似性与差异性，准确聚合最相关观测位置的信息，为未观测位置生成归纳表示。进而设计关系感知门控循环单元网络，自适应捕获每种关系下序列表示中的时间相关性。最后提出多关系注意力机制，动态融合来自多种关系、不同时间步的复杂时空信息以计算预测输出。在三个真实世界数据集上的实验表明，所提模型一致优于现有最先进方法，且在观测位置数量较少时优势更加显著。代码已开源：https://github.com/zhengchuanpan/INCREASE。