Despite the prevalence of recent success in learning from static graphs, learning from time-evolving graphs remains an open challenge. In this work, we design new, more stringent evaluation procedures for link prediction specific to dynamic graphs, which reflect real-world considerations, to better compare the strengths and weaknesses of methods. First, we create two visualization techniques to understand the reoccurring patterns of edges over time and show that many edges reoccur at later time steps. Based on this observation, we propose a pure memorization baseline called EdgeBank. EdgeBank achieves surprisingly strong performance across multiple settings because easy negative edges are often used in the current evaluation setting. To evaluate against more difficult negative edges, we introduce two more challenging negative sampling strategies that improve robustness and better match real-world applications. Lastly, we introduce six new dynamic graph datasets from a diverse set of domains missing from current benchmarks, providing new challenges and opportunities for future research. Our code repository is accessible at https://github.com/fpour/DGB.git.
翻译:尽管最近从静态图表中学习的成功非常普遍,但从时间变化的图表中学习仍然是一项公开的挑战。在这项工作中,我们设计了新的、更严格的评价程序,将具体预测与反映现实世界考虑的动态图表联系起来,以更好地比较方法的优缺点。首先,我们创造两种视觉化技术,以了解长期边缘的复发模式,并显示在以后的步骤中许多边缘重新出现。根据这一观察,我们提议了一个纯的记忆化基线,称为EdgeBank。EdgeBank在多个环境中取得了惊人的强效,因为在当前评估环境中经常使用容易的负边缘。为了评估更困难的负面边缘,我们引入了两种更具挑战性的负面抽样战略,以提高稳健性,更好地匹配现实世界的应用。最后,我们从从从现有基准所缺的不同领域引入了六套新的动态图表数据集,为未来的研究提供了新的挑战和机遇。我们的代码库可在https://github.com/fpour/DGB.git查阅。