Computes hybrid centrality of each node in a network
hybrid(A, BC = c("standard", "random"), beta)An adjacency matrix of network data
How should the betweenness centrality be computed?
Defaults to "random".
Set to "standard" for standard betweenness.
Beta parameter to be passed to the rspbc function
Defaults to .01
A vector of hybrid centrality values for each node in the network (higher values are more central, lower values are more peripheral)
Christensen, A. P., Kenett, Y. N., Aste, T., Silvia, P. J., & Kwapil, T. R. (2018). Network structure of the Wisconsin Schizotypy Scales-Short Forms: Examining psychometric network filtering approaches. Behavior Research Methods, 50, 2531-2550.
Pozzi, F., Di Matteo, T., & Aste, T. (2013). Spread of risk across financial markets: Better to invest in the peripheries. Scientific Reports, 3, 1655.
# NOT RUN {
# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A
HC <- hybrid(A)
# }
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