netclu_infomap: Infomap community finding
In bioregion: Comparison of Bioregionalisation Methods

netclu_infomap

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 = "2.8.0",
  binpath = "tempdir",
  check_install = TRUE,
  path_temp = "infomap_temp",
  delete_temp = TRUE
)

Arguments

`net`	The output object from `similarity()` or `dissimilarity_to_similarity()`. If a `data.frame` is used, the first two columns represent pairs of sites (or any pair of nodes), and the next column(s) are the similarity indices.
`weight`	A `boolean` indicating if the weights should be considered if there are more than two columns.
`cut_weight`	A minimal weight value. If `weight` is TRUE, the links between sites with a weight strictly lower than this value will not be considered (`0` by default).
`index`	The name or number of the column to use as weight. By default, the third column name of `net` is used.
`seed`	The 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 result in fewer modules (`1` by default).
`numtrials`	For the number of trials before picking up the best solution.
`twolevel`	A `boolean` indicating if the algorithm should optimize a two-level partition of the network (`FALSE` by default for multi-level).
`show_hierarchy`	A `boolean` specifying if the hierarchy of community should be identifiable in the outputs (`FALSE` by default).
`directed`	A `boolean` indicating if the network is directed (from column 1 to column 2).
`bipartite_version`	A `boolean` indicating if the bipartite version of Infomap should be used (see Note).
`bipartite`	A `boolean` indicating if the network is bipartite (see Note).
`site_col`	The name or number for the column of site nodes (i.e. primary nodes).
`species_col`	The name or number for the column of species nodes (i.e. feature nodes).
`return_node_type`	A `character` indicating what types of nodes (`"site"`, `"species"`, or `"both"`) should be returned in the output (`"both"` by default).
`version`	A `character` indicating the Infomap version to use.
`binpath`	A `character` indicating the path to the bin folder (see install_binaries and Details).
`check_install`	A `boolean` indicating if the function should check that the Infomap has been properly installed (see install_binaries and Details).
`path_temp`	A `character` indicating the path to the temporary folder (see Details).
`delete_temp`	A `boolean` indicating if the temporary folder should be removed (see Details).

Details

Infomap is a network clustering algorithm based on the Map equation proposed in Rosvall & 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.

If you did not use install_binaries to change the permissions and test the binary files PLEASE MAKE SURE to set check_install accordingly.

The C++ version of Infomap generates temporary folders and/or files that are stored in the path_temp folder ("infomap_temp" with a unique timestamp located in the bin folder in binpath by default). This temporary folder is removed by default (delete_temp = TRUE).

Several versions of Infomap are available in the package. See install_binaries for more details.

Value

A list of class bioregion.clusters with five slots:

name: A character containing the name of the algorithm.
args: A list of input arguments as provided by the user.
inputs: A list of characteristics of the clustering process.
algorithm: A list of all objects associated with the clustering procedure, such as original cluster objects.
clusters: A data.frame containing the clustering results.

In the algorithm slot, users can find the following elements:

cmd: The command line used to run Infomap.
version: The Infomap version.
web: 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 a 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, "site" to preserve only the site nodes, and "species" to preserve only the species nodes.

Author(s)

Maxime Lenormand (maxime.lenormand@inrae.fr)
Pierre Denelle (pierre.denelle@gmail.com)
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, 1118-1123.

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)

bioregion documentation built on April 12, 2025, 9:13 a.m.