There has been recent success in learning from static graphs, but despite their prevalence, learning from time-evolving graphs remains challenging. We design new, more stringent evaluation procedures for link prediction specific to dynamic graphs, which reflect real-world considerations and can better compare different methods' strengths and weaknesses. In particular, we create two visualization techniques to understand the recurring patterns of edges over time. They show that many edges reoccur at later time steps. Therefore, we propose a pure memorization baseline called EdgeBank. It achieves surprisingly strong performance across multiple settings, partly due to the easy negative edges used in the current evaluation setting. Hence, we introduce two more challenging negative sampling strategies that improve robustness and can better match real-world applications. Lastly, we introduce five new dynamic graph datasets from a diverse set of domains missing from current benchmarks, providing new challenges and opportunities for future research.

提出了新的评估过程和数据集，以更好地比较不同方法在时间演化图中的强弱，并提出了EdgeBank纯记忆基线，该基线有效地解决了当前评估设置中容易的负面边问题，并通过提出更具挑战性的负采样策略改进了鲁棒性和更好地匹配了现实世界的应用。

动态网络链接预测的更佳评估