Journal article
Applying centrality measures to impact analysis: A coauthorship network analysis
Journal of the American Society for Information Science and Technology, v 60(10), pp 2107-2118
Oct 2009
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.
Metrics
Details
- Title
- Applying centrality measures to impact analysis: A coauthorship network analysis
- Creators
- Erjia YanYing Ding
- Publication Details
- Journal of the American Society for Information Science and Technology, v 60(10), pp 2107-2118
- Publisher
- Wiley Subscription Services, Inc., A Wiley Company; Hoboken
- Number of pages
- 12
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000270250900014
- Scopus ID
- 2-s2.0-70349308371
- Other Identifier
- 991014976894104721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Information Systems
- Information Science & Library Science