Despite the increasing use of citation-based metrics for research evaluation purposes, we do not know yet which metrics best deliver on their promise to gauge the significance of a scientific paper or a patent. We assess 17 network-based metrics by their ability to identify milestone papers and patents in three large citation datasets. We find that traditional information-retrieval evaluation metrics are strongly affected by the interplay between the age distribution of the milestone items and age biases of the evaluated metrics. Outcomes of these metrics are therefore not representative of the metrics’ ranking ability. We argue in favor of a modified evaluation procedure that explicitly penalizes biased metrics and allows us to reveal metrics’ performance patterns that are consistent across the datasets. PageRank and LeaderRank turn out to be the best-performing ranking metrics when their age bias is suppressed by a simple transformation of the scores that they produce, whereas other popular metrics, including citation count, HITS and Collective Influence, produce significantly worse ranking results.

尽管出于研究评估目的越来越多地使用基于引文的指标，但我们仍不知道哪种指标最能兑现其对科学论文或专利重要性的承诺。我们通过17种基于网络的指标来评估其在三个大型引用数据集中识别里程碑论文和专利的能力。我们发现，传统的信息检索评估指标受到里程碑项目的年龄分布与评估指标的年龄偏差之间相互作用的强烈影响。因此，这些指标的结果不能代表指标的排名能力。我们主张采用修改后的评估程序，该程序明确惩罚有偏见的指标，并允许我们揭示指标在整个数据集中的绩效模式。