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）扩展到时序图基准（TGB），对TGB进行了实证评估。相较于原有基准，我们纳入了十一种主流动态图学习方法以实现更全面的对比。实验发现：（1）不同模型在不同数据集上展现出差异化的表现，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报告的结果可获得显著提升。本研究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，并提供可直接供后续研究参考的实验结果。本项目所有资源均公开于https://github.com/yule-BUAA/DyGLib_TGB。本工作仍在完善中，欢迎学界同仁反馈建议以持续改进。