netclu_infomap {bioregion} | R Documentation |
Infomap community finding
Description
This function finds communities in a (un)weighted (un)directed network based on the Infomap algorithm (https://github.com/mapequation/infomap).
Usage
netclu_infomap(
net,
weight = TRUE,
cut_weight = 0,
index = names(net)[3],
seed = NULL,
nbmod = 0,
markovtime = 1,
numtrials = 1,
twolevel = FALSE,
show_hierarchy = FALSE,
directed = FALSE,
bipartite_version = FALSE,
bipartite = FALSE,
site_col = 1,
species_col = 2,
return_node_type = "both",
version = "2.7.1",
binpath = "tempdir",
path_temp = "infomap_temp",
delete_temp = TRUE
)
Arguments
net |
the output object from |
weight |
a |
cut_weight |
a minimal weight value. If |
index |
name or number of the column to use as weight. By default,
the third column name of |
seed |
for the random number generator (NULL for random by default). |
nbmod |
penalize solutions the more they differ from this number (0 by default for no preferred number of modules). |
markovtime |
scales link flow to change the cost of moving between modules, higher values results in fewer modules (default is 1). |
numtrials |
for the number of trials before picking up the best solution. |
twolevel |
a |
show_hierarchy |
a |
directed |
a |
bipartite_version |
a |
bipartite |
a |
site_col |
name or number for the column of site nodes (i.e. primary nodes). |
species_col |
name or number for the column of species nodes (i.e. feature nodes). |
return_node_type |
a |
version |
a |
binpath |
a |
path_temp |
a |
delete_temp |
a |
Details
Infomap is a network clustering algorithm based on the Map equation proposed in (Rosvall and Bergstrom 2008) that finds communities in (un)weighted and (un)directed networks.
This function is based on the C++ version of Infomap (https://github.com/mapequation/infomap/releases). This function needs binary files to run. They can be installed with install_binaries.
If you changed the default path to the bin
folder
while running install_binaries PLEASE MAKE SURE to set binpath
accordingly.
The C++ version of Infomap generates temporary folders and/or files that are
stored in the path_temp
folder ("infomap_temp" with an unique timestamp
located in the bin folder in binpath
by default). This temporary folder is
removed by default (delete_temp = TRUE
).
Several version of Infomap are available in the package. See install_binaries for more details.
Value
A list
of class bioregion.clusters
with five slots:
name:
character
containing the name of the algorithmargs:
list
of input arguments as provided by the userinputs:
list
of characteristics of the clustering processalgorithm:
list
of all objects associated with the clustering procedure, such as original cluster objectsclusters:
data.frame
containing the clustering results
In the algorithm
slot, users can find the following elements:
cmd
: the command line use to run Infomapversion
: the Infomap versionweb
: Infomap's GitHub repository
Note
Infomap has been designed to deal with bipartite networks. To use this
functionality set the bipartite_version
argument to TRUE in order to
approximate a two-step random walker (see
https://www.mapequation.org/infomap/ for more information). Note that
a bipartite network can also be considered as unipartite network
(bipartite = TRUE
).
In both cases do not forget to indicate which of the first two columns is
dedicated to the site nodes (i.e. primary nodes) and species nodes (i.e.
feature nodes) using the arguments site_col
and species_col
.
The type of nodes returned in the output can be chosen with the argument
return_node_type
equal to both
to keep both types of nodes, sites
to preserve only the sites nodes and species
to preserve only the
species nodes.
Author(s)
Maxime Lenormand (maxime.lenormand@inrae.fr), Pierre Denelle (pierre.denelle@gmail.com) and Boris Leroy (leroy.boris@gmail.com)
References
Rosvall M, Bergstrom CT (2008). “Maps of random walks on complex networks reveal community structure.” Proceedings of the National Academy of Sciences, 105(4), 1118–1123.
See Also
install_binaries, netclu_louvain, netclu_oslom
Examples
comat <- matrix(sample(1000, 50), 5, 10)
rownames(comat) <- paste0("Site", 1:5)
colnames(comat) <- paste0("Species", 1:10)
net <- similarity(comat, metric = "Simpson")
com <- netclu_infomap(net)