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
this https URL.

本文提出了一种基于图表示学习的时空克里金模型，通过编码不同观察位置间的空间相似性、功能相似性和转移概率，结合门控循环神经网络和多种关系注意机制，从多个时间步骤中动态融合不同关系的时空信息，成功解决了基于异质空间关系的时空克里金问题。