  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 to apply 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 twenty years
(1988-2007), we construct an evolving coauthorship network and calculate four
centrality measures (closeness, betweenness, degree and PageRank) for authors
in this network. We find out 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.
