Spatial-temporal graph neural network for traffic forecasting: An overview and open research issues

Machine Learning of Spatial Data

“…Graph neural networks were first introduced by [149]. The main idea behind graph neural networks is to aggregate features from neighborhood nodes to represent the feature value of a node [150]. This process is similar to applying convolutions on regular grids.…”

“…These approaches are relatively new and have shown impressive performance in many applications, such as disease spread forecasting [156], traffic analysis [157], medical diagnosis and analysis [158], fraud detection [148], and natural language processing [159]. Spatial methods process the entire graph simultaneously and are computationally less expensive [150]. Graph attention neural networks are similar to the spatial graph neural networks but they assign larger weights (more attention) to the important nodes in the neighborhood [150].…”

“…Spatial methods process the entire graph simultaneously and are computationally less expensive [150]. Graph attention neural networks are similar to the spatial graph neural networks but they assign larger weights (more attention) to the important nodes in the neighborhood [150]. Ref.…”

“…Spatial dependencies are assumed fixed with connectivity and distance in all time-steps for most of the current spatio-temporal graph neural networks. However, this assumption does not hold for many real-world applications, such as traffic prediction in a road network, where spatial dependencies change dynamically based on factors, such as accidents or weather conditions [150]. A recent survey of machine learning methods of spatiotemporal sequences is available elsewhere for interested readers [185].…”

Effective vaccination strategy using graph neural network ansatz

“…Graph neural networks (GNNs) are deep learning-based methods that operate on graphs or networks where other types of machine-learning methods such as convolutional neural networks (CNNs) or recurrent neural network (RNNs) cannot be implemented because of the irregular and non-Euclidean nature of the complex network. GNN has become a widely used method for network analysis because of its convincing performance in various fields, such as estimation of molecular properties [30,31], drug discovery [32], and traffic forecasting [33,34]. In the epidemic field, GNNs have been employed for the prediction of disease prevalence [35][36][37], identification of patient zero [38], and estimation of epidemic state using limited information [39].…”