A recently released Temporal Graph Benchmark is analyzed in the context of
Dynamic Link Property Prediction. We outline our observations and propose a
trivial optimization-free baseline of "recently popular nodes" outperforming
other methods on all medium and large-size datasets in the Temporal Graph
Benchmark. We propose two measures based on Wasserstein distance which can
quantify the strength of short-term and long-term global dynamics of datasets.
By analyzing our unexpectedly strong baseline, we show how standard negative
sampling evaluation can be unsuitable for datasets with strong temporal
dynamics. We also show how simple negative-sampling can lead to model
degeneration during training, resulting in impossible to rank, fully saturated
predictions of temporal graph networks. We propose improved negative sampling
schemes for both training and evaluation and prove their usefulness. We conduct
a comparison with a model trained non-contrastively without negative sampling.
Our results provide a challenging baseline and indicate that temporal graph
network architectures need deep rethinking for usage in problems with
significant global dynamics, such as social media, cryptocurrency markets or
e-commerce. We open-source the code for baselines, measures and proposed
negative sampling schemes.

最近发布的时间图基准被分析在动态链接属性预测的背景下，我们提出了一个简单的无优化基线，即 “最近流行节点”，在时间图基准的所有中型和大型数据集上优于其他方法。我们提出了两种基于 Wasserstein 距离的度量方式，可以量化数据集的短期和长期全局动态的强度。通过分析我们出乎意料的强基线，我们展示了标准的负采样评估对于具有强时间动态的数据集可能不适用。我们还展示了简单的负采样在训练过程中可能导致模型退化，导致无法排名的、完全饱和的预测结果。我们提出了改进的负采样方案，用于训练和评估，并证明了它们的有用性。我们与非对比训练的模型进行了比较，没有进行负采样。我们的结果提供了一个具有挑战性的基准，并表明时间图网络架构在具有重大全局动态的问题中，如社交媒体、加密货币市场或电子商务中，需要进行深入的思考。我们公开发表了基线、测量和提出的负采样方案的代码。

时序图模型无法捕捉全局时序动态

Temporal graph models fail to capture global temporal dynamics

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。

时间图基准的实证评估

An Empirical Evaluation of Temporal Graph Benchmark

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。