Applying centrality measures to impact analysis: A coauthorship network analysis

TL;DR

This study applies centrality measures to coauthorship networks in LIS to evaluate author impact, finding significant correlations with citation counts and suggesting their usefulness for impact analysis.

Contribution

It introduces a micro-level approach using centrality measures for impact analysis in coauthorship networks, which is less explored in prior research.

Findings

Centrality measures significantly correlate with citation counts.

Four centrality measures are effective indicators of author impact.

Centrality measures can be used for author ranking in impact analysis.

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 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.