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 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.

翻译：一篇近期发布的时间图基准在动态链接属性预测的背景下进行了分析。我们概述了观察结果，并提出了一种基于“近期流行节点”的平凡无优化基线方法，该方法在时间图基准的中型和大型数据集上优于其他方法。我们提出了两个基于瓦瑟斯坦距离的度量，能够量化数据集的短期和长期全局动态强度。通过分析这一出乎意料的强基线，我们展示了标准负采样评估如何不适用于具有强时间动态特征的数据集。同时阐明了简单的负采样如何导致训练过程中的模型退化，从而产生无法排序、完全饱和的时间图网络预测结果。我们提出了一种改进的负采样方案（同时用于训练和评估），并证明了其有效性。我们与不使用负采样的非对比训练模型进行了比较。我们的结果提供了一个具有挑战性的基线，并表明时间图网络架构需要针对存在显著全局动态的问题（如社交媒体、加密货币市场或电子商务）进行深刻反思。我们开源了基线方法、度量指标及所提出的负采样方案的相关代码。