rspbc: Randomized Shortest Paths Betweenness Centrality

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

Description

Computes betweenness centrality based on randomized shortest paths of each node in a network (Please see and cite Kivimaki et al., 2016)

Usage

rspbc(A, beta = 0.01, comm = NULL)

Arguments

A	An adjacency matrix of network data
beta	Sets the beta parameter. Defaults to 0.01 (recommended). Beta > 0.01 measure gets closer to weighted betweenness centrality (10) and beta < 0.01 measure gets closer to degree (.0001)
comm	Vector. Community vector containing a value for each node. Computes "bridge" RSPBC, where the number of times a node is used on a random path between to another community

Value

A vector of randomized shortest paths betweenness centrality values for each node in the network

Author(s)

Alexander Christensen <[e-mail]>

References

Kivimaki, I., Lebichot, B., Saramaki, J., & Saerens, M. (2016). Two betweenness centrality measures based on Randomized Shortest Paths. Scientific Reports, 6, 19668.

Examples

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

rspbc <- rspbc(A, beta=0.01)

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