stable: Stabilizing Nodes

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

Description

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

Usage

stable(
  A,
  comm = c("walktrap", "louvain"),
  cent = c("betweenness", "rspbc", "closeness", "strength", "degree", "hybrid"),
  absolute = TRUE,
  diagonal = 0,
  ...
)

Arguments

A	An adjacency matrix of network data
comm	Can be a vector of community 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".
absolute	Should network use absolute weights? Defaults to TRUE. Set to FALSE for signed weights
diagonal	Sets the diagonal values of the A input. Defaults to 0
...	Additional arguments for cluster_walktrap and louvain community detection algorithms

Value

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

Author(s)

Alexander Christensen <[e-mail]>

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, 5854.

Examples

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

stabilizing <- stable(A, comm = "walktrap")

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