使用作者权重方案在 JMIR mHealth 和 uHealth 上发表论文的被引频次最高的作者：文献计量分析。

BACKGROUND: Many previous papers have investigated most-cited articles or most productive authors in academics, but few have studied most-cited authors. Two challenges are faced in doing so, one of which is that some different authors will have the same name in the bibliometric data, and the second is that coauthors' contributions are different in the article byline. No study has dealt with the matter of duplicate names in bibliometric data. Although betweenness centrality (BC) is one of the most popular degrees of density in social network analysis (SNA), few have applied the BC algorithm to interpret a network's characteristics. A quantitative scheme must be used for calculating weighted author credits and then applying the metrics in comparison. OBJECTIVE: This study aimed to apply the BC algorithm to examine possible identical names in a network and report the most-cited authors for a journal related to international mobile health (mHealth) research. METHODS: We obtained 676 abstracts from Medline based on the keywords "JMIR mHealth and uHealth" (Journal) on June 30, 2018. The author names, countries/areas, and author-defined keywords were recorded. The BCs were then calculated for the following: (1) the most-cited authors displayed on Google Maps; (2) the geographical distribution of countries/areas for the first author; and (3) the keywords dispersed by BC and related to article topics in comparison on citation indices. Pajek software was used to yield the BC for each entity (or node). Bibliometric indices, including h-, g-, and x-indexes, the mean of core articles on g(Ag)=sum (citations on g-core/publications on g-core), and author impact factor (AIF), were applied. RESULTS: We found that the most-cited author was Sherif M Badawy (from the United States), who had published six articles on JMIR mHealth and uHealth with high bibliometric indices (h=3; AIF=8.47; x=4.68; Ag=5.26). We also found that the two countries with the highest BC were the United States and the United Kingdom and that the two keyword clusters of mHealth and telemedicine earned the highest indices in comparison to other counterparts. All visual representations were successfully displayed on Google Maps. CONCLUSIONS: The most cited authors were selected using the authorship-weighted scheme (AWS), and the keywords of mHealth and telemedicine were more highly cited than other counterparts. The results on Google Maps are novel and unique as knowledge concept maps for understanding the feature of a journal. The research approaches used in this study (ie, BC and AWS) can be applied to other bibliometric analyses in the future.

背景：许多先前的论文都研究了学术界的高被引文章或高产作者，但很少有研究高被引作者的。在这样做时面临两个挑战，其中一个是在文献计量数据中，一些不同的作者可能具有相同的姓名，第二个是在文章署名中，共同作者的贡献不同。没有研究涉及文献计量数据中重复名称的问题。尽管中介中心度（BC）是社会网络分析（SNA）中最流行的密度度之一，但很少有人将 BC 算法应用于解释网络的特征。必须使用定量方案来计算加权作者信用，并在比较中应用该指标。

目的：本研究旨在应用 BC 算法检查网络中可能存在的相同名称，并报告与国际移动健康（mHealth）研究相关的期刊的高被引作者。

方法：我们于 2018 年 6 月 30 日基于“JMIR mHealth 和 uHealth”（期刊）的关键字从 Medline 中获取了 676 篇摘要。记录了作者姓名、国家/地区和作者定义的关键字。然后计算了以下内容的 BC：（1）在 Google 地图上显示的最受引用的作者；（2）第一作者的国家/地区的地理分布；（3）BC 分散的关键字与引文索引中文章主题相关。Pajek 软件用于为每个实体（或节点）产生 BC。应用了文献计量学指标，包括 h-，g-和 x-指数，g 核心文章的平均值（g 核心上的引文/ g 核心上的出版物）和作者影响因子（AIF）。

结果：我们发现，最受引用的作者是 Sherif M Badawy（来自美国），他在 JMIR mHealth 和 uHealth 上发表了六篇高文献计量学指标的文章（h=3；AIF=8.47；x=4.68；Ag=5.26）。我们还发现，BC 最高的两个国家是美国和英国，与其他对应物相比，mHealth 和远程医疗这两个关键字集群的指标最高。所有的可视化表示都成功地显示在 Google 地图上。

结论：使用作者权重方案（AWS）选择了被引最多的作者，并且 mHealth 和远程医疗的关键字比其他对应物被引更高。Google 地图上的结果是新颖而独特的，因为它们是理解期刊特征的知识概念图。本研究中使用的研究方法（即 BC 和 AWS）可应用于未来的其他文献计量分析。