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Identifying key papers within a journal via network centrality measures

Author

Listed:
  • Saikou Y. Diallo

    (Old Dominion University)

  • Christopher J. Lynch

    (Old Dominion University)

  • Ross Gore

    (Old Dominion University)

  • Jose J. Padilla

    (Old Dominion University)

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.

Suggested Citation

  • Saikou Y. Diallo & Christopher J. Lynch & Ross Gore & Jose J. Padilla, 2016. "Identifying key papers within a journal via network centrality measures," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1005-1020, June.
    • Handle: RePEc:spr:scient:v:107:y:2016:i:3:d:10.1007_s11192-016-1891-8
    DOI: 10.1007/s11192-016-1891-8
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