Hybrid Centrality
Computes hybrid centrality of each node in a network
hybrid(A, BC = c("standard", "random"), beta)A |
An adjacency matrix of network data |
BC |
How should the betweenness centrality be computed?
Defaults to |
beta |
Beta parameter to be passed to the |
A vector of hybrid centrality values for each node in the network (higher values are more central, lower values are more peripheral)
Alexander Christensen <alexpaulchristensen@gmail.com>
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.
# Pearson's correlation only for CRAN checks A <- TMFG(neoOpen, normal = FALSE)$A HC <- hybrid(A)
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