论文标题
在链接预测中区分无阈值评估指标的能力
Discriminating abilities of threshold-free evaluation metrics in link prediction
论文作者
论文摘要
链接预测是网络科学中的一个范式且具有挑战性的问题，它试图根据已知拓扑来揭示丢失的链接或预测未来的链接。一个基本但仍未解决的问题是如何选择适当的指标来公平评估预测算法。接收器操作特征曲线（AUC）和平衡精度（BP）下的区域是早期研究中最受欢迎的两个指标，而它们的有效性最近在辩论中。同时，Precision-Recall曲线（AUPR）下的区域变得越来越流行，尤其是在生物学研究中。基于具有可调噪声和可预测性的玩具模型，我们提出了一种测量任何给定度量的区分能力的方法。我们将此方法应用于上述三个无阈值指标，表明AUC和AUPR比BP更具区分性，而AUC比AUPR更具区别。结果表明，最好同时使用AUC和AUPR评估链接预测算法，同时，它警告我们，仅基于BP的评估可能是不真实的。本文提供了有关链接预测和其他分类问题评估指标有效性的全面图案的起点。
Link prediction is a paradigmatic and challenging problem in network science, which attempts to uncover missing links or predict future links, based on known topology. A fundamental but still unsolved issue is how to choose proper metrics to fairly evaluate prediction algorithms. The area under the receiver operating characteristic curve (AUC) and the balanced precision (BP) are the two most popular metrics in early studies, while their effectiveness is recently under debate. At the same time, the area under the precision-recall curve (AUPR) becomes increasingly popular, especially in biological studies. Based on a toy model with tunable noise and predictability, we propose a method to measure the discriminating abilities of any given metric. We apply this method to the above three threshold-free metrics, showing that AUC and AUPR are remarkably more discriminating than BP, and AUC is slightly more discriminating than AUPR. The result suggests that it is better to simultaneously use AUC and AUPR in evaluating link prediction algorithms, at the same time, it warns us that the evaluation based only on BP may be unauthentic. This article provides a starting point towards a comprehensive picture about effectiveness of evaluation metrics for link prediction and other classification problems.
