Identifying key papers within a journal via network centrality measures.
Saikou Y Diallo, Christopher J Lynch, Ross Gore, Jose J Padilla
Author Information
Saikou Y Diallo: Virginia Modeling Analysis and Simulation Center, Old Dominion University, 1030 University Boulevard, Suffolk, VA 23435 USA.
Christopher J Lynch: Virginia Modeling Analysis and Simulation Center, Old Dominion University, 1030 University Boulevard, Suffolk, VA 23435 USA. ORCID
Ross Gore: Virginia Modeling Analysis and Simulation Center, Old Dominion University, 1030 University Boulevard, Suffolk, VA 23435 USA.
Jose J Padilla: Virginia Modeling Analysis and Simulation Center, Old Dominion University, 1030 University Boulevard, Suffolk, VA 23435 USA.
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.
