Computes the between-community strength for each node in the network
comcat(
A,
comm = c("walktrap", "louvain"),
cent = c("strength", "degree"),
absolute = TRUE,
metric = c("across", "each"),
diagonal = 0,
...
)
An adjacency matrix of network data
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
Centrality measure to be used.
Defaults to "strength"
.
Should network use absolute weights?
Defaults to TRUE
.
Set to FALSE
for signed weights
Whether the metric should be compute for across all of the communities
(a single value) or for each community (a value for each community).
Defaults to "across"
.
Set to "each"
for values for each community
Sets the diagonal values of the A
input.
Defaults to 0
Additional arguments for cluster_walktrap
and louvain
community detection algorithms
A vector containing the between-community strength value for each node
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.
# NOT RUN {
# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A
communicating <- comcat(A, comm = "walktrap", cent = "strength", metric = "across")
# }
Run the code above in your browser using DataLab