Recent progress in research on Deep Graph Networks (DGNs) has led to a maturation of the domain of learning on graphs. Despite the growth of this research field, there are still important challenges that are yet unsolved. Specifically, there is an urge of making DGNs suitable for predictive tasks on realworld systems of interconnected entities, which evolve over time. With the aim of fostering research in the domain of dynamic graphs, at first, we survey recent advantages in learning both temporal and spatial information, providing a comprehensive overview of the current state-of-the-art in the domain of representation learning for dynamic graphs. Secondly, we conduct a fair performance comparison among the most popular proposed approaches, leveraging rigorous model selection and assessment for all the methods, thus establishing a sound baseline for evaluating new architectures and approaches

翻译：近年来，深度图网络（DGNs）的研究进展推动了图学习领域的成熟。尽管该研究领域不断壮大，但仍有若干重要挑战亟待解决。具体而言，迫切需要使DGNs适用于随时间演化的真实世界互联实体系统的预测任务。为促进动态图领域的研究，本文首先综述了同时学习时空信息的最新进展，全面概述了动态图表征学习领域的当前最先进方法。其次，我们对最广泛提出的方法进行了公平的性能比较，对所有方法采用了严格的模型选择与评估流程，从而为评估新架构和新方法建立了可靠的基准。