Dynamic Graph Neural Networks (GNNs) combine temporal information with GNNs to capture structural, temporal, and contextual relationships in dynamic graphs simultaneously, leading to enhanced performance in various applications. As the demand for dynamic GNNs continues to grow, numerous models and frameworks have emerged to cater to different application needs. There is a pressing need for a comprehensive survey that evaluates the performance, strengths, and limitations of various approaches in this domain. This paper aims to fill this gap by offering a thorough comparative analysis and experimental evaluation of dynamic GNNs. It covers 81 dynamic GNN models with a novel taxonomy, 12 dynamic GNN training frameworks, and commonly used benchmarks. We also conduct experimental results from testing representative nine dynamic GNN models and three frameworks on six standard graph datasets. Evaluation metrics focus on convergence accuracy, training efficiency, and GPU memory usage, enabling a thorough comparison of performance across various models and frameworks. From the analysis and evaluation results, we identify key challenges and offer principles for future research to enhance the design of models and frameworks in the dynamic GNNs field.

翻译：动态图神经网络（GNNs）通过将时序信息与GNN相结合，能够同时捕获动态图中的结构、时序与上下文关联，从而在各类应用中提升性能。随着对动态GNN需求的持续增长，为满足不同应用需求，众多模型与框架应运而生。当前亟需一份全面综述，评估该领域各类方法的性能、优势与局限性。本文旨在填补这一空白，对动态GNN进行深入的比较分析与实验评估。研究涵盖81个动态GNN模型（采用新型分类体系）、12个动态GNN训练框架及常用基准测试集。我们还在六个标准图数据集上对9个代表性动态GNN模型与3个框架进行了实验测试。评估指标聚焦于收敛精度、训练效率及GPU内存占用，从而实现对不同模型与框架性能的全面对比。基于分析与评估结果，我们识别出关键挑战，并为动态GNN领域未来模型与框架的设计优化提供了研究原则。