Description Usage Arguments Details Value Author(s) References See Also Examples
Function that searches for and visualizes communitystructures in graphs.
1 2 3  Communities(P, graph = TRUE, lay = "layout_with_fr", coords = NULL,
Vsize = 15, Vcex = 1, Vcolor = "orangered",
VBcolor = "darkred", VLcolor = "black", main = "")

P 
Sparsified precision 
graph 
A 
lay 
A 
coords 
A 
Vsize 
A 
Vcex 
A 
Vcolor 
A 
VBcolor 
A 
VLcolor 
A 
main 
A 
Communities in a network are groups of vertices (modules) that are densely connected within. Community search is performed by the GirvanNewman algorithm (Newman and Girvan, 2004).
When graph = TRUE
the community structure in the graph is visualized.
The default layout is according to the FruchtermanReingold algorithm (1991).
Most layout functions supported by igraph
are supported (the function is partly a wrapper around certain igraph
functions).
The igraph layouts can be invoked by a character
that mimicks a call to a igraph
layout functions in the lay
argument.
When using lay = NULL
one can specify the placement of vertices with the coords
argument.
The row dimension of this matrix should equal the number of vertices.
The column dimension then should equal 2 (for 2D layouts) or 3 (for 3D layouts).
The coords
argument can also be viewed as a convenience argument as it enables one, e.g., to layout a graph
according to the coordinates of a previous call to Ugraph
.
If both the the lay and the coords arguments are not NULL
, the lay argument takes precedence.
Communities are indicated by color markings.
An object of class list:
membership 

modularityscore 

When graph = TRUE
the function also returns a graph.
Carel F.W. Peeters <[email protected]>
Csardi, G. and Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems 1695. http://igraph.sf.net
Fruchterman, T.M.J., and Reingold, E.M. (1991). Graph Drawing by ForceDirected Placement. Software: Practice & Experience, 21: 11291164.
Newman, M. and Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69: 026113.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  ## Obtain some (highdimensional) data
p = 25
n = 10
set.seed(333)
X = matrix(rnorm(n*p), nrow = n, ncol = p)
colnames(X)[1:25] = letters[1:25]
## Obtain regularized precision under optimal penalty
OPT < optPenalty.LOOCV(X, lambdaMin = .5, lambdaMax = 30, step = 100)
## Determine support regularized standardized precision under optimal penalty
PC0 < sparsify(symm(OPT$optPrec), threshold = "localFDR")$sparseParCor
## Search and visualize communities
Commy < Communities(PC0)

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