In recent years, Dynamic Graph (DG) representations have been increasingly used for modeling dynamic systems due to their ability to integrate both topological and temporal information in a compact representation. Dynamic graphs allow to efficiently handle applications such as social network prediction, recommender systems, traffic forecasting or electroencephalography analysis, that can not be adressed using standard numeric representations. As a direct consequence of the emergence of dynamic graph representations, dynamic graph learning has emerged as a new machine learning problem, combining challenges from both sequential/temporal data processing and static graph learning. In this research area, Dynamic Graph Neural Network (DGNN) has became the state of the art approach and plethora of models have been proposed in the very recent years. This paper aims at providing a review of problems and models related to dynamic graph learning. The various dynamic graph supervised learning settings are analysed and discussed. We identify the similarities and differences between existing models with respect to the way time information is modeled. Finally, general guidelines for a DGNN designer when faced with a dynamic graph learning problem are provided.

翻译：近年来，动态图（DG）表示因能紧凑地整合拓扑与时间信息，已被广泛用于动态系统建模。动态图可有效处理社交网络预测、推荐系统、交通预测或脑电图分析等应用，而这些应用无法通过标准数值表示方法解决。随着动态图表示的出现，动态图学习已成为融合序列/时间数据处理和静态图学习挑战的新机器学习问题。在该研究领域中，动态图神经网络（DGNN）已成为最先进方法，近年来涌现出大量模型。本文旨在综述动态图学习相关问题与模型，分析并讨论多种有监督动态图学习设置，识别现有模型在时间信息建模方式上的异同，最后为DGNN设计者面对动态图学习问题提供通用指导原则。