INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging

by   Chuanpan Zheng, et al.
Xiamen University
Zhejiang University
The University of Melbourne

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 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


Forecasting Unobserved Node States with spatio-temporal Graph Neural Networks

Forecasting future states of sensors is key to solving tasks like weathe...

Spatio-Temporal Relation and Attention Learning for Facial Action Unit Detection

Spatio-temporal relations among facial action units (AUs) convey signifi...

Keyword-Aware Relative Spatio-Temporal Graph Networks for Video Question Answering

The main challenge in video question answering (VideoQA) is to capture a...

Spatio-Temporal Graph Representation Learning for Fraudster Group Detection

Motivated by potential financial gain, companies may hire fraudster grou...

Heterogeneous Temporal Graph Neural Network

Graph neural networks (GNNs) have been broadly studied on dynamic graphs...

Joint Inductive and Transductive Learning for Video Object Segmentation

Semi-supervised video object segmentation is a task of segmenting the ta...

A Socio-Demographic Latent Space Approach to Spatial Data When Geography is Important but Not All-Important

Many models for spatial and spatio-temporal data assume that "near thing...

Please sign up or login with your details

Forgot password? Click here to reset