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 on node and edge-level tasks, 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（DGN）研究的进展推动了图学习领域的成熟。尽管该研究领域不断发展，但仍存在亟待解决的重要挑战。具体而言，亟需使深度图网络能够适用于随时间演化的互联实体真实世界系统中的预测任务。为促进动态图领域的研究，本文首先综述了同时学习时间与空间信息的最新进展，全面梳理了当前动态图表示学习领域的研究现状。其次，我们在节点级和边级任务上对最主流的几种方法进行了公平的性能比较，通过严格模型选择与评估为所有方法建立统一基准，从而为评估新架构与研究方法奠定可靠基础。