Identifying key papers within a journal via network centrality measures
Published in Scientometrics, 2016
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.
Recommended citation: Diallo, Saikou Y; Lynch, Christopher J; Gore, Ross; Padilla, Jose J. (2016). "Identifying key papers within a journal via network centrality measures". Scientometrics. 107, 1005-1020.
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