Identifying key papers within a journal via network centrality measures

Saikou Y. Diallo, Christopher J. Lynch (), Ross Gore and Jose J. Padilla
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Saikou Y. Diallo: Old Dominion University
Christopher J. Lynch: Old Dominion University
Ross Gore: Old Dominion University
Jose J. Padilla: Old Dominion University

Scientometrics, 2016, vol. 107, issue 3, No 6, 1005-1020

Abstract: Abstract 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.

Keywords: Paper filtering; Article-level metrics; Network centrality analysis; 65F15 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11192-016-1891-8

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