We present the Temporal Graph Benchmark (TGB), a collection of challenging
and diverse benchmark datasets for realistic, reproducible, and robust
evaluation of machine learning models on temporal graphs. TGB datasets are of
large scale, spanning years in duration, incorporate both node and edge-level
prediction tasks and cover a diverse set of domains including social, trade,
transaction, and transportation networks. For both tasks, we design evaluation
protocols based on realistic use-cases. We extensively benchmark each dataset
and find that the performance of common models can vary drastically across
datasets. In addition, on dynamic node property prediction tasks, we show that
simple methods often achieve superior performance compared to existing temporal
graph models. We believe that these findings open up opportunities for future
research on temporal graphs. Finally, TGB provides an automated machine
learning pipeline for reproducible and accessible temporal graph research,
including data loading, experiment setup and performance evaluation. TGB will
be maintained and updated on a regular basis and welcomes community feedback.
TGB datasets, data loaders, example codes, evaluation setup, and leaderboards
are publicly available at this https URL .

Temporal Graph Benchmark 是一个收集了各种涉及社交、贸易、交通等网络领域的大规模数据集，用于实现机器学习模型在时间图上的现实、可重复和强大的评估。此外，该研究表明对于动态节点属性预测任务，简单的方法往往比现有的时间图模型表现更好，并提供了一个自动化的机器学习流程用于可重复和可访问的时间图研究。