  Graph Neural Networks have gained huge interest in the past few years. These
powerful algorithms expanded deep learning models to non-Euclidean space and
were able to achieve state of art performance in various applications including
recommender systems and social networks. However, this performance is based on
static graph structures assumption which limits the Graph Neural Networks
performance when the data varies with time. Spatiotemporal Graph Neural
Networks are extension of Graph Neural Networks that takes the time factor into
account. Recently, various Spatiotemporal Graph Neural Network algorithms were
proposed and achieved superior performance compared to other deep learning
algorithms in several time dependent applications. This survey discusses
interesting topics related to Spatiotemporal Graph Neural Networks, including
algorithms, applications, and open challenges.
