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This function finds communities in a (un)weighted (un)directed network based on the Infomap algorithm (


  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



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.


a boolean indicating if the weights should be considered if there are more than two columns.


a minimal weight value. If weight is TRUE, the links between sites with a weight strictly lower than this value will not be considered (O by default).


name or number of the column to use as weight. By default, the third column name of net is used.


for the random number generator (NULL for random by default).


penalize solutions the more they differ from this number (0 by default for no preferred number of modules).


scales link flow to change the cost of moving between modules, higher values results in fewer modules (default is 1).


for the number of trials before picking up the best solution.


a boolean indicating if the algorithm should optimize a two-level partition of the network (default is multi-level).


a boolean specifying if the hierarchy of community should be identifiable in the outputs (FALSE by default).


a boolean indicating if the network is directed (from column 1 to column 2).


a boolean indicating if the bipartite version of Infomap should be used (see Note).


a boolean indicating if the network is bipartite (see Note).


name or number for the column of site nodes (i.e. primary nodes).


name or number for the column of species nodes (i.e. feature nodes).


a character indicating what types of nodes (site, species or both) should be returned in the output (return_node_type = "both" by default).


a character indicating the Infomap version to use.


a character indicating the path to the bin folder (see install_binaries and Details).


a character indicating the path to the temporary folder (see Details).


a boolean indicating if the temporary folder should be removed (see Details).


A list of class bioregion.clusters with five slots:

  1. name: character containing the name of the algorithm

  2. args: list of input arguments as provided by the user

  3. inputs: list of characteristics of the clustering process

  4. algorithm: list of all objects associated with the clustering procedure, such as original cluster objects

  5. clusters: data.frame containing the clustering results

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

  • cmd: the command line use to run Infomap

  • version: the Infomap version

  • web: Infomap's GitHub repository


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 ( 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.


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 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.


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.


Maxime Lenormand (, Pierre Denelle ( and Boris Leroy (


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)
#> Infomap 2.7.1 is not installed... Please have a look at
#>     https//
#>     for more details.
#> It should be located in /tmp/RtmpA0YX5i/bin/INFOMAP/2.7.1/