In this paper, we conduct an empirical evaluation of Temporal Graph Benchmark (TGB) by extending our Dynamic Graph Library (DyGLib) to TGB. Compared with TGB, we include eleven popular dynamic graph learning methods for more exhaustive comparisons. Through the experiments, we find that (1) different models depict varying performance across various datasets, which is in line with previous observations; (2) the performance of some baselines can be significantly improved over the reported results in TGB when using DyGLib. This work aims to ease the researchers' efforts in evaluating various dynamic graph learning methods on TGB and attempts to offer results that can be directly referenced in the follow-up research. All the used resources in this project are publicly available at https://github.com/yule-BUAA/DyGLib_TGB. This work is in progress, and feedback from the community is welcomed for improvements.

翻译：本文通过将我们的动态图库（DyGLib）扩展至时间图基准（TGB），对其进行了实证评估。相较于TGB，我们纳入了十一种流行的动态图学习方法以实现更全面的比较。实验发现：(1) 不同模型在各数据集上表现出不同的性能，这与先前观察结果一致；(2) 使用DyGLib时，部分基线方法的性能相较于TGB报告结果有显著提升。本工作旨在简化研究人员在TGB上评估各类动态图学习方法的流程，并试图提供可直接参考的结果供后续研究使用。本项目中使用的所有资源已公开于https://github.com/yule-BUAA/DyGLib_TGB。该工作仍在进展中，欢迎学界反馈以持续改进。