Diallo Saikou Y, Lynch Christopher J, Gore Ross, Padilla Jose J
Virginia Modeling Analysis and Simulation Center, Old Dominion University, 1030 University Boulevard, Suffolk, VA 23435 USA.
Scientometrics. 2016;107(3):1005-1020. doi: 10.1007/s11192-016-1891-8. Epub 2016 Feb 15.
This article examines the extent to which existing network centrality measures can be used (1) as filters to identify a set of papers to start reading within a journal and (2) as article-level metrics to identify the relative importance of a paper within a journal. We represent a dataset of published papers in the Public Library of Science (PLOS) via a co-citation network and compute three established centrality metrics for each paper in the network: closeness, betweenness, and eigenvector. Our results show that the network of papers in a journal is scale-free and that eigenvector centrality (1) is an effective filter and article-level metric and (2) correlates well with citation counts within a given journal. However, closeness centrality is a poor filter because articles fit within a small range of citations. We also show that betweenness centrality is a poor filter for journals with a narrow focus and a good filter for multidisciplinary journals where communities of papers can be identified.
（1）一种筛选器，以在期刊内识别出一组可供开始阅读的论文；（2）一种文章层面的指标，以识别一篇论文在期刊内的相对重要性。我们通过共被引网络来表示科学公共图书馆（PLOS）中已发表论文的数据集，并为网络中的每篇论文计算三种既定的中心性度量：接近中心性、中介中心性和特征向量中心性。我们的结果表明，期刊中的论文网络是无标度的，并且特征向量中心性：（1）是一种有效的筛选器和文章层面的指标；（2）与给定期刊内的被引频次具有良好的相关性。然而，接近中心性是一种较差的筛选器，因为文章的被引频次范围较窄。我们还表明，中介中心性对于专注度较窄的期刊是一种较差的筛选器，而对于可以识别出论文群落的多学科期刊则是一种良好的筛选器。
