| Type: | Package |
| Title: | Fast Network Modularity and Roles Computation by Simulated Annealing (Rgraph C Library Wrapper for R) |
| Version: | 0.2.6 |
| Description: | Provides functions to compute the modularity and modularity-related roles in networks. It is a wrapper around the rgraph library (Guimera & Amaral, 2005, <doi:10.1038/nature03288>). |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| LazyLoad: | no |
| SystemRequirements: | GNU GSL |
| NeedsCompilation: | yes |
| Suggests: | testthat, knitr, rmarkdown, igraph |
| VignetteBuilder: | knitr |
| RoxygenNote: | 7.2.1 |
| Packaged: | 2023-01-16 21:15:30 UTC; stouffer |
| Author: | Daniel B. Stouffer [cre, aut, ths] (Maintainer), Guilhem Doulcier [aut] (R bindings, current implementation of the simulated annealing algorithm), Roger Guimera [ctb] (Author of the original rgraph library) |
| Maintainer: | Daniel B. Stouffer <daniel.stouffer@canterbury.ac.nz> |
| Repository: | CRAN |
| Date/Publication: | 2023-01-16 21:50:02 UTC |
Computes modularity and modularity roles from a network.
Description
Compute modularity and modularity roles for graphs using simulated annealing
Usage
netcarto(
web,
seed = as.integer(floor(runif(1, 1, 100000001))),
iterfac = 1,
coolingfac = 0.995,
bipartite = FALSE
)
Arguments
web |
network either as a square adjacency matrix or a list describing E interactions a->b: the first (resp. second) element is the vector of the labels of a (resp. b), the third (optional) is the vector of interaction weights. |
seed |
Seed for the random number generator: Must be a positive integer. |
iterfac |
At each temperature of the simulated annealing (SA), the program performs fN^2 individual-node updates (involving the movement of a single node from one module to another) and fN collective updates (involving the merging of two modules and the split of a module). The number "f" is the iteration factor. |
coolingfac |
Temperature cooling factor. |
bipartite |
If True use the bipartite definition of modularity. |
Value
A list. The first element is a dataframe with the name, module, z-score, and participation coefficient for each row of the input matrix. The second element is the modularity of this partition.
Examples
# Generate a simple random network
a = matrix(as.integer(runif(100)<.3), ncol=10)
a[lower.tri(a)] = 0
# Find an optimal partition for modularity using netcarto.
netcarto(a)