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.

本文通过对81个动态GNN模型、12个动态GNN训练框架和常用基准进行了全面的比较分析和实验评估，在对六个标准图数据集上测试了九个代表性的动态GNN模型和三个框架。评估指标包括收敛精度、训练效率和GPU内存使用情况，从而实现了对各种模型和框架性能的全面比较。通过分析和评估结果，我们确定了关键挑战，并提出了未来研究的原则，以增强动态GNN领域模型和框架的设计。

动态图神经网络综述：模型、框架、基准、实验与挑战