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) some issues
need to be addressed in the current version of TGB, including mismatched data
statistics, inaccurate evaluation metric computation, and so on; (2) different
models depict varying performance across various datasets, which is in line
with previous observations; (3) 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 this https URL This work is
in progress, and feedback from the community is welcomed for improvements.

通过扩展我们的动态图库 (DyGLib) 到 Temporal Graph Benchmark (TGB)，我们对 TGB 进行了经验评估。在实验中，我们发现一些问题需要解决，包括数据统计不匹配、评估指标计算不准确等；不同模型在各个数据集上表现不同，这与以前的观察相符；当使用 DyGLib 时，一些基准的表现可以显著提高。该工作旨在简化研究人员在 TGB 上评估各种动态图学习方法的工作，并试图提供可以直接参考的结果。