一位入,两位出:只包括外部引用次数而不包括引用关系,可大大改进基于网络的论文衡量标准

IF 2.3 4区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY
Systems Pub Date : 2024-09-17 DOI:10.3390/systems12090377
Jianlin Zhou, Zhesi Shen, Jinshan Wu
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引用次数: 0

摘要

为识别高影响力论文,人们提出了许多排名算法和衡量标准。直接引用计数和基于网络的类似 PageRank 的算法都很常用。理想情况下,引文网络数据越完整,排名的信息量就越大。然而,获取更多关于引文关系的数据往往成本高昂且极具挑战性。在某些情况下,获取引文数量可能相对简单。在本文中,我们将研究使用额外的引文次数,但不使用额外的引文关系,以形成更有参考价值的指标来识别高影响力论文。例如,我们建议将本地引文网络与来自更完整数据源的附加引文计数相结合,从而增强原始 PageRank 算法。我们将这种增强方法应用于美国物理学会(APS)的论文,以验证其有效性。结果表明,所提出的排名算法对缺失数据具有很强的鲁棒性,并能提高对高质量论文的识别能力。这表明,在相对较小的引文网络上计算基于网络的度量,只需加入引文计数的额外数据,而不加入额外的引文关系,就有可能提高其有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
One-Bit In, Two-Bit Out: Network-Based Metrics of Papers Can Be Largely Improved by Including Only the External Citation Counts without the Citation Relations
Many ranking algorithms and metrics have been proposed to identify high-impact papers. Both the direct citation counts and the network-based PageRank-like algorithms are commonly used. Ideally, the more complete the data on the citation network, the more informative the ranking. However, obtaining more data on citation relations is often costly and challenging. In some cases, obtaining the citation counts can be relatively simple. In this paper, we look into using the additional citation counts but without additional citation relations to form more informative metrics for identifying high-impact papers. As an example, we propose enhancing the original PageRank algorithm by combining the local citation network with the additional citation counts from a more complete data source. We apply this enhanced method to American Physical Society (APS) papers to verify its effectiveness. The results indicate that the proposed ranking algorithm is robust against missing data and can improve the identification of high-quality papers. This shows that it is possible to enhance the effectiveness of a network-based metric calculated on a relatively small citation network by including only the additional data of the citation counts, without the additional citation relations.
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来源期刊
Systems
Systems Decision Sciences-Information Systems and Management
CiteScore
2.80
自引率
15.80%
发文量
204
审稿时长
11 weeks
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