Center for Complex Network Research, Department of Physics, Northeastern University, Boston, MA 02115, USA.
Science. 2013 Oct 4;342(6154):127-32. doi: 10.1126/science.1237825.
The lack of predictability of citation-based measures frequently used to gauge impact, from impact factors to short-term citations, raises a fundamental question: Is there long-term predictability in citation patterns? Here, we derive a mechanistic model for the citation dynamics of individual papers, allowing us to collapse the citation histories of papers from different journals and disciplines into a single curve, indicating that all papers tend to follow the same universal temporal pattern. The observed patterns not only help us uncover basic mechanisms that govern scientific impact but also offer reliable measures of influence that may have potential policy implications.
基于引文的指标(如影响因子和短期引文)常用于衡量影响力,但它们的可预测性较低,这引发了一个基本问题:引文模式是否具有长期可预测性?在这里,我们为单个论文的引文动态推导了一个机械模型,使我们能够将来自不同期刊和学科的论文的引文历史压缩成一条单一的曲线,表明所有论文都倾向于遵循相同的普遍时间模式。观察到的模式不仅帮助我们揭示了影响科学影响力的基本机制,还提供了可靠的影响力衡量标准,这些标准可能具有潜在的政策意义。