Being a kind of non-Euclidean data,spatiotemporal graph data exists everywhere from trafficflow,air quality index to crime case,etc.Unlike the raster data,the irregular and disordered characteristics of spatiotemporal...Being a kind of non-Euclidean data,spatiotemporal graph data exists everywhere from trafficflow,air quality index to crime case,etc.Unlike the raster data,the irregular and disordered characteristics of spatiotemporal graph data have attracted the research interest of scholars,with the prediction of spatiotemporal graph data being one of the research hot spots.The emergence of spatiotemporal graph neural networks(ST-GNNs)provides a new insight for solving the problem of obtaining spatial correlation for spatiotemporal graph data prediction while achieving state-of-the-art performance.In this paper,comprehensive survey of research on ST-GNNs prediction domain isa presented,where the background of ST-GNNs is introduced before the computational paradigm of ST-GNN is thoroughly reviewed.From the perspective of model construction,59 well-known models in recent years are classified and discussed.Some of these models are further analyzed in terms of performance and efficiency.Subsequently,the categories and applicationfields of spatiotemporal graph data are summarized,providing a clear idea of technology selection for different applications.Finally,the evolution history and future direction of ST-GNNs are also summarized,to facilitate future researchers to timely understand the current state of prediction research by ST-GNNs.展开更多
基金supported by National Social Science Fund of China[grant number 21JCA004]Soft Science Research Project of Ministry of Housing and Urban-Rural Development of China[grant number R20200287]Open Research Fund of Key Laboratory of Digital Cartography and Land Information Application,Ministry of Natural Resources[grant number ZRZYBWD202102].
文摘Being a kind of non-Euclidean data,spatiotemporal graph data exists everywhere from trafficflow,air quality index to crime case,etc.Unlike the raster data,the irregular and disordered characteristics of spatiotemporal graph data have attracted the research interest of scholars,with the prediction of spatiotemporal graph data being one of the research hot spots.The emergence of spatiotemporal graph neural networks(ST-GNNs)provides a new insight for solving the problem of obtaining spatial correlation for spatiotemporal graph data prediction while achieving state-of-the-art performance.In this paper,comprehensive survey of research on ST-GNNs prediction domain isa presented,where the background of ST-GNNs is introduced before the computational paradigm of ST-GNN is thoroughly reviewed.From the perspective of model construction,59 well-known models in recent years are classified and discussed.Some of these models are further analyzed in terms of performance and efficiency.Subsequently,the categories and applicationfields of spatiotemporal graph data are summarized,providing a clear idea of technology selection for different applications.Finally,the evolution history and future direction of ST-GNNs are also summarized,to facilitate future researchers to timely understand the current state of prediction research by ST-GNNs.
