hybrid: Hybrid Centrality

In NetworkToolbox: Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis

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)

Example output

Package: NetworkToolbox: Methods and Measures for Brain, Cognitive, and Psychometric
Network Analysis
Version: 1.4.0
Created on: 2020-03-08
Maintainer: Alexander P. Christensen, University of North Carolina at Greensboro
Contributors: Guido Previde Massara, University College London
 For citation information, type citation("NetworkToolbox")
 For vignettes, see <https://doi.org/10.32614/RJ-2018-065> 
 For bugs and errors, submit an issue to <https://github.com/AlexChristensen/NetworkToolbox/issues>

NetworkToolbox documentation built on May 28, 2021, 5:11 p.m.