NetworkToolbox (version 1.4.2)

rspbc: Randomized Shortest Paths Betweenness Centrality

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

References

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

Examples

# NOT RUN {
# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

rspbc <- rspbc(A, beta=0.01)

# }