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Identifying key papers within a journal via network centrality measures
Saikou Y Diallo1, Christopher J Lynch1, Ross Gore1
1Virginia Modeling Analysis and Simulation Center, Old Dominion University, 1030 University Boulevard, Suffolk, VA 23435 USA.
Scientometrics
|March 28, 2020
Summary
Eigenvector centrality effectively identifies important papers within journals, acting as both a filter and a metric. Other centrality measures like closeness and betweenness show limitations depending on journal focus.
Area of Science:
- Bibliometrics
- Network Science
- Scholarly Communication
Background:
- Identifying influential research papers is crucial for navigating scientific literature.
- Network centrality measures are potential tools for assessing paper importance.
Purpose of the Study:
- To evaluate network centrality measures (closeness, betweenness, eigenvector) as filters and article-level metrics within journals.
- To determine the effectiveness of these measures in identifying key publications and their relative importance.
Main Methods:
- Constructed co-citation networks for papers published in the Public Library of Science (PLOS).
- Calculated closeness, betweenness, and eigenvector centrality for each paper.
- Analyzed the scale-free nature of the paper network.
Main Results:
- Eigenvector centrality proved effective as both a filter and an article-level metric, correlating well with citation counts.
- Closeness centrality was a poor filter due to a narrow citation range for articles.
- Betweenness centrality's effectiveness varied: poor for narrow-focus journals, good for multidisciplinary journals identifying paper communities.
Conclusions:
- Eigenvector centrality is a valuable tool for identifying important papers within journals.
- The utility of betweenness centrality depends on the journal's scope, being more effective in multidisciplinary settings.
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