Interest in social network analysis has exploded in the past few years, partly thanks to the advancements in statistical methods and computing for network analysis. A wide range of the methods for network analysis is already covered by existent R packages. However, no comprehensive packages are available to calculate group centrality scores and to identify key players (i.e., those players who constitute the most central group) in a network. These functionalities are important because, for example, many social and health interventions rely on key players to facilitate the intervention. Identifying key players is challenging because players who are individually the most central are not necessarily the most central as a group due to redundancy in their connections. In this paper we develop methods and tools for computing group centrality scores and for identifying key players in social networks. We illustrate the methods using both simulated and empirical examples. The package keyplayer providing the presented methods is available from Comprehensive R Archive Network (CRAN).
network, sna, igraph, statnet, RSiena, keyplayer, influenceR
SocialSciences, gR, Optimization, Bayesian, Graphics, Spatial
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For attribution, please cite this work as
An & Liu, "The R Journal: keyplayer: An R Package for Locating Key Players in Social Networks", The R Journal, 2016
BibTeX citation
@article{RJ-2016-018,
author = {An, Weihua and Liu, Yu-Hsin},
title = {The R Journal: keyplayer: An R Package for Locating Key Players in Social Networks},
journal = {The R Journal},
year = {2016},
note = {https://doi.org/10.32614/RJ-2016-018},
doi = {10.32614/RJ-2016-018},
volume = {8},
issue = {1},
issn = {2073-4859},
pages = {257-268}
}