Identifying key papers within a journal via network centrality measures

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 publishe...

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Bibliographic Details
Published in:Scientometrics 2016, Vol.107 (3), p.1005-1020
Main Authors: Diallo, Saikou Y., Lynch, Christopher J., Gore, Ross, Padilla, Jose J.
Format: Article
Language:English
Subjects:
Citation analysis
Computer Science
Eigenvectors
Information Storage and Retrieval
Library Science
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Summary: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.
ISSN:0138-9130
1588-2861
DOI:10.1007/s11192-016-1891-8