A simple equation for rank-citation profiles

IF 3.5 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Informetrics Pub Date : 2025-03-25 DOI:10.1016/j.joi.2025.101660

Y.C. Tay , Akarsh Srivastava , Mostafa Rezazad , Hamid Sarbazi-Azad

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Abstract

There is considerable interest in the citation count for an author's publications. This has led to many proposals for citation indices to characterize rank-citation profiles, which order an author's publications by their citation count. However, there is so far no tractable model to facilitate the analysis of these profiles and the design of their indices. This paper presents a simple equation for such design and analysis.

The equation has three parameters that are calibrated by three geometrical characteristics of a rank-citation profile, namely the maximum number of citations for a publication (M), the number of cited publications (N), and the Hirsch index (h). The equation's simple form makes it tractable for analyzing rank-citation profiles and indices.

To demonstrate, the equation is used to derive closed-form approximations (in terms of M, N and h) for various indices; these expressions provide new insight into previous index analyses, the influence of a profile's tail, and the effect of time.

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一个简单的排名-引用概况方程

人们对作者的出版物的引用数很感兴趣。这导致了许多关于引文索引的建议，以表征引文排名概况，即根据作者的引文数量对其出版物进行排序。然而，到目前为止，还没有一个易于处理的模型来促进这些概况的分析和它们的指数的设计。本文给出了这种设计和分析的一个简单公式。该方程有三个参数，这些参数由排名-引文概况的三个几何特征校准，即出版物的最大被引次数(M)，被引出版物的数量(N)和赫希指数(h)。该方程的简单形式使其易于分析排名-引文概况和索引。为了证明，该方程用于推导各种指标的封闭形式近似（以M， N和h表示）；这些表达式为以前的指数分析、曲线尾部的影响以及时间的影响提供了新的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Informetrics Social Sciences-Library and Information Sciences

CiteScore

6.40

自引率

16.20%

发文量

期刊介绍： Journal of Informetrics (JOI) publishes rigorous high-quality research on quantitative aspects of information science. The main focus of the journal is on topics in bibliometrics, scientometrics, webometrics, patentometrics, altmetrics and research evaluation. Contributions studying informetric problems using methods from other quantitative fields, such as mathematics, statistics, computer science, economics and econometrics, and network science, are especially encouraged. JOI publishes both theoretical and empirical work. In general, case studies, for instance a bibliometric analysis focusing on a specific research field or a specific country, are not considered suitable for publication in JOI, unless they contain innovative methodological elements.