Open AccessApplying centrality measures to impact analysis: A coauthorship network analysis
Author(s) -
Yan Erjia,
Ding Ying
Publication year - 2009
Publication title -
journal of the american society for information science and technology
Language(s) - English
Resource type - Journals
eISSN - 1532-2890
pISSN - 1532-2882
DOI - 10.1002/asi.21128
Subject(s) - centrality , betweenness centrality , computer science , network science , pagerank , network analysis , construct (python library) , katz centrality , data science , social network analysis , network theory , ranking (information retrieval) , information retrieval , data mining , complex network , world wide web , mathematics , statistics , physics , quantum mechanics , social media , programming language
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.