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. Temporal Graph Neural Networks are extension of Graph Neural Networks that takes the time factor into account. Recently, various Temporal 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 Spatio temporal Graph Neural Networks, including algorithms, application, and open challenges.
翻译:过去几年来,神经网络图获得了巨大的兴趣。这些强大的算法将深层次学习模型扩展至非欧洲空间,并得以在包括推荐者系统和社交网络在内的各种应用中达到最新水平。然而,这种表现是基于静态图形结构假设的,在数据随时间变化时限制了图形神经网络的性能。时空图形神经网络是图形神经网络的延伸,考虑到时间因素。最近,在几个依赖时间的应用中,提出了各种Temoral图形神经网络算法,并取得了优于其他深层次学习算法的性能。这次调查讨论了与Spatio时间图神经网络有关的有趣主题,包括算法、应用和公开挑战。