
Computes betweenness centrality based on randomized shortest paths of each node in a network (Please see and cite Kivimaki et al., 2016)
rspbc(A, beta = 0.01, comm = NULL)
An adjacency matrix of network data
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)
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
A vector of randomized shortest paths betweenness centrality values for each node in the network
Kivimaki, I., Lebichot, B., Saramaki, J., & Saerens, M. (2016). Two betweenness centrality measures based on Randomized Shortest Paths. Scientific Reports, 6, 19668.
# NOT RUN {
# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A
rspbc <- rspbc(A, beta=0.01)
# }
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