Deep learning on spatiotemporal graphs: A systematic review, methodological landscape, and research opportunities

Abstract

Deep learning approaches, given their low cost and high reliability, have gained much popularity in different subjects, such as computer vision and natural language processing, and more recently in graph data types. Spatiotemporal graph-based neural networks have been more and more developed to solve problems related to spatiotemporal data, mainly for analyzing changes over time and for provisioning purposes. In this systematic literature review, we have aimed to answer the most important questions regarding spatiotemporal graph deep learning architectures in different applications domains, such as traffic related topics, medical imaging, and geographical data analysis. We have selected more than 50 papers that cover a wide range of applications and very different architectures and innovations. We have also noticed that most of them consider the spatiotemporal graphs to be quite classic graphs without any further sophisticated modeling of temporal and spatial data evolution. Moreover, core problems (such as node classification, frequent pattern recognition, etc.) and application domains are not sufficiently addressed by the state-of-the-art. This study thus opens many perspectives to new developments in spatio-temporal graph deep learning with different strategies in order to solve various end-to-end tasks and other problems related to this special kind of graph.