论文标题
为动态链接预测更好地评估
Towards Better Evaluation for Dynamic Link Prediction
论文作者
论文摘要
尽管最近从静态图中学习的成功率很高，但从时间不断发展的图中学习仍然是一个开放的挑战。在这项工作中，我们为特定于动态图的链接预测设计了新的，更严格的评估程序，这些链接预测反映了现实世界的注意事项，以更好地比较方法的优势和劣势。首先，我们创建了两种可视化技术，以了解随着时间的流逝的重新发生的边缘模式，并表明以后的时间步骤会重新出现许多边缘。基于此观察，我们提出了一个称为EdgeBank的纯记忆基线。 EdgeBank在多个设置中实现了令人惊讶的出色性能，因为在当前评估设置中经常使用简单的负边缘。为了评估更困难的负面边缘，我们引入了另外两种具有挑战性的负面抽样策略，可以提高鲁棒性并更好地匹配现实世界的应用。最后，我们从当前基准中缺少各种域中介绍了六个新的动态图数据集，从而为未来的研究提供了新的挑战和机遇。我们的代码存储库可在https://github.com/fpour/dgb.git上访问。
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.
