| Communities {rags2ridges} | R Documentation |
Search and visualize community-structures
Description
Function that searches for and visualizes community-structures in graphs.
Usage
Communities(
P,
graph = TRUE,
lay = "layout_with_fr",
coords = NULL,
Vsize = 15,
Vcex = 1,
Vcolor = "orangered",
VBcolor = "darkred",
VLcolor = "black",
main = ""
)
Arguments
P |
Sparsified precision |
graph |
A |
lay |
A |
coords |
A |
Vsize |
A |
Vcex |
A |
Vcolor |
A |
VBcolor |
A |
VLcolor |
A |
main |
A |
Details
Communities in a network are groups of vertices (modules) that are densely connected within. Community search is performed by the Girvan-Newman algorithm (Newman and Girvan, 2004).
When graph = TRUE the community structure in the graph is visualized.
The default layout is according to the Fruchterman-Reingold 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.
Value
An object of class list:
membership |
|
modularityscore |
|
When graph = TRUE the function also returns a graph.
Author(s)
Carel F.W. Peeters <carel.peeters@wur.nl>
References
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 Force-Directed Placement. Software: Practice & Experience, 21: 1129-1164.
Newman, M. and Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69: 026113.
See Also
Examples
## Obtain some (high-dimensional) 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)