NetworkToolbox (version 1.1.2)

stable: Stabilizing Nodes

Description

Computes the within-community centrality for each node in the network

Usage

stable(A, factors = c("walktrap", "louvain"), cent = c("betweenness",
  "rspbc", "strength", "degree", "hybrid"), ...)

Arguments

A

An adjacency matrix of network data

factors

Can be a vector of factor assignments or community detection algorithms ("walktrap" or "louvain") can be used to determine the number of factors. Defaults to "walktrap". Set to "louvain" for louvain community detection

cent

Centrality measure to be used. Defaults to "strength".

...

Additional arguments for community detection algorithms

Value

A matrix containing the within-community centrality value for each node

References

Blanken, T. F., Deserno, M. K., Dalege, J., Borsboom, D., Blanken, P., Kerkhof, G. A., & Cramer, A. O. (2018). The role of stabilizing and communicating symptoms given overlapping communities in psychopathology networks. Scientific Reports, 8(1), 5854.

Examples

# NOT RUN {
A<-TMFG(neoOpen)$A

stabilizing <- stable(A, factors = "walktrap")
# }