hybrid {NetworkToolbox}

Hybrid Centrality

Description

Computes hybrid centrality of each node in a network

Usage

hybrid(A, BC = c("standard", "random"), beta)

Arguments

A

An adjacency matrix of network data

BC

How should the betweenness centrality be computed? Defaults to "random". Set to "standard" for standard betweenness.

beta

Beta parameter to be passed to the rspbc function Defaults to .01

Value

A vector of hybrid centrality values for each node in the network (higher values are more central, lower values are more peripheral)

Author(s)

Alexander Christensen <[e-mail]>

References

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.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

HC <- hybrid(A)