There is a fast-growing body of research on predicting future links in dynamic networks, with many new algorithms. Some benchmark data exists, and performance evaluations commonly rely on comparing the scores of observed network events (positives) with those of randomly generated ones (negatives). These evaluation measures depend on both the predictive ability of the model and, crucially, the type of negative samples used. Besides, as generally the case with temporal data, prediction quality may vary over time. This creates a complex evaluation space. In this work, we catalog the possibilities for negative sampling and introduce novel visualization methods that can yield insight into prediction performance and the dynamics of temporal networks. We leverage these visualization tools to investigate the effect of negative sampling on the predictive performance, at the node and edge level. We validate empirically, on datasets extracted from recent benchmarks that the error is typically not evenly distributed across different data segments. Finally, we argue that such visualization tools can serve as powerful guides to evaluate dynamic link prediction methods at different levels.

翻译：动态网络中的未来链路预测研究正快速增长，众多新算法不断涌现。现有基准数据集通常通过将观测到的网络事件（正样本）与随机生成的样本（负样本）的分数进行比较来评估性能。这些评估指标不仅取决于模型的预测能力，更关键地取决于所采用的负样本类型。此外，作为时序数据的普遍特征，预测质量会随时间变化，从而形成复杂的评估空间。本文系统梳理了负采样方法的可能性，并引入新型可视化技术以揭示预测性能与时序网络动态特性。我们利用这些可视化工具，从节点和边两个层面探究负采样对预测性能的影响。基于近期基准数据集进行的实证验证表明，误差通常未在不同数据片段间均匀分布。最后，我们论证了此类可视化工具可作为多层级评估动态链路预测方法的有力指导。