| 结合时间切片信息的作者共引分析方法与实证 |
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| 引用本文： | 黄文彬,王冰璐,步一,王煜晨.结合时间切片信息的作者共引分析方法与实证[J].图书情报工作,2017,61(16):135-142. |
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| 作者姓名： | 黄文彬 王冰璐 步一 王煜晨 |
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| 作者单位： | 1. 北京大学信息管理系 北京 100871;
2. 印第安纳大学信息学、计算机与工程学院 布鲁明顿 47408 |
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| 摘 要： | 目的/意义]传统作者共引分析（ACA）方法将领域发展视为一个整体，忽略领域发展期间的变化，导致知识图谱解读会产生一定的偏差。本文旨在引入时间变量，找出领域发展期间的转变关键节点，并以此作为时间切片的划分依据，利用ACA绘制每个时间切片内部的知识图谱，观察领域内的子领域发展与核心作者的变化。方法/过程]首先通过作者的年度发文比例对时间切片进行选取，借鉴经济学均线理论对曲线做平滑处理，选取曲线变化度较高的年份作为转变节点切割时间段，并对每个时间切片内进行ACA的运算与结果分析。结果/结论]结果显示，随着时间的变迁，领域知识图谱发生了相应的变化，利用作者发文比例选择时间切点进行综合时间切片的作者共引分析提高了聚类结果的群聚性，且有助于挖掘出科学共同体的更多细节。
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| 关 键 词： | 作者共引分析 共引分析 引文分析 信息计量学 |
| 收稿时间： | 2017-07-07 |
Author Co-citation Analysis Combined with the Time Slice Information:Method and Experiment |
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| Huang Wenbin,Wang Binglu,Bu Yi,Wang Yuchen.Author Co-citation Analysis Combined with the Time Slice Information:Method and Experiment[J].Library and Information Service,2017,61(16):135-142. |
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| Authors: | Huang Wenbin Wang Binglu Bu Yi Wang Yuchen |
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| Institution: | 1. Department of Information Management, Peking University, Beijing 100871;
2. School of Informatics, Computing, and Engineering, Indiana University, Bloomington, Indiana 47408, U. S. A |
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| Abstract: | Purpose/significance] Traditional author co-citation analysis regards the domain development as a whole, ignoring that the rapid field changes in the research period would exert deviation impact on the knowledge domain mapping.Method/process] To solve this problem, this paper proposed the publishing time variable to anatomize the vital turning as the alternation for each time snippet. Furthermore, we could visualize each period so as to compare their differences and conclude the transformation tendency of this domain by visualizing them separately. Specifically, this article selected vital turning points according to publishing ratios for each author annually, during which the time variation curve should be smoothed by the average theory.Result/conclusion] The result points out that the knowledge domain map has altered relatively with the time transition. Additionally, the clustering effect has been improved in specific period and more details on the scientific community could be detected. |
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| Keywords: | author co-citation analysis co-citation analysis citation analysis informetrics |
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