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Applying centrality measures to impact analysis: A coauthorship network analysis

✍ Scribed by Erjia Yan; Ying Ding

Publisher
John Wiley and Sons
Year
2009
Tongue
English
Weight
416 KB
Volume
60
Category
Article
ISSN
1532-2882

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✦ Synopsis

Abstract

Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro‐level network properties with the aim of applying centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of 20 years (1988–2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness centrality, betweenness centrality, degree centrality, and PageRank) for authors in this network. We find that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking and suggest that centrality measures can be useful indicators for impact analysis.

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