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Louvain community finding

Source: R/netclu_louvain.R
netclu_louvain.Rd

This function finds communities in a (un)weighted undirected network based on the Louvain algorithm.

Usage

netclu_louvain(
  net,
  weight = TRUE,
  cut_weight = 0,
  index = names(net)[3],
  lang = "igraph",
  resolution = 1,
  seed = NULL,
  q = 0,
  c = 0.5,
  k = 1,
  bipartite = FALSE,
  site_col = 1,
  species_col = 2,
  return_node_type = "both",
  binpath = "tempdir",
  check_install = TRUE,
  path_temp = "louvain_temp",
  delete_temp = TRUE,
  algorithm_in_output = 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.

lang

A string indicating which version of Louvain should be used ("igraph" or "cpp", see Details).

resolution

A resolution parameter to adjust the modularity (1 is chosen by default, see Details).

seed

The random number generator seed (only when lang = "igraph", NULL for random by default).

q

The quality function used to compute the partition of the graph (modularity is chosen by default, see Details).

c

The parameter for the Owsinski-Zadrozny quality function (between 0 and 1, 0.5 is chosen by default).

k

The kappa_min value for the Shi-Malik quality function (it must be > 0, 1 is chosen by default).

bipartite

A boolean indicating if the network is bipartite (see Details).

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).

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 Louvain 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).

algorithm_in_output

A boolean indicating if the original output of cluster_louvain should be returned in the output (TRUE by default, see Value).

Value

A list of class bioregion.clusters with five slots:

  1. name: A character containing the name of the algorithm.

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

  3. inputs: A list of characteristics of the clustering process.

  4. algorithm: A list of all objects associated with the clustering procedure, such as original cluster objects (only if algorithm_in_output = TRUE).

  5. clusters: A data.frame containing the clustering results.

In the algorithm slot, if algorithm_in_output = TRUE, users can find the output of cluster_louvain if lang = "igraph" and the following element if lang = "cpp":

  • cmd: The command line used to run Louvain.

  • version: The Louvain version.

  • web: The Louvain's website.

Details

Louvain is a network community detection algorithm proposed in (Blondel et al., 2008). This function offers two implementations of the Louvain algorithm (controlled by the lang parameter): the igraph implementation (cluster_louvain) and the C++ implementation (https://sourceforge.net/projects/louvain/, version 0.3).

The igraph implementation allows adjustment of the resolution parameter of the modularity function (resolution argument) used internally by the algorithm. Lower values typically yield fewer, larger clusters. The original definition of modularity is recovered when the resolution parameter is set to 1 (by default).

The C++ implementation provides several quality functions: q = 0 for the classical Newman-Girvan criterion (Modularity), q = 1 for the Zahn-Condorcet criterion, q = 2 for the Owsinski-Zadrozny criterion (parameterized by c), q = 3 for the Goldberg Density criterion, q = 4 for the A-weighted Condorcet criterion, q = 5 for the Deviation to Indetermination criterion, q = 6 for the Deviation to Uniformity criterion, q = 7 for the Profile Difference criterion, q = 8 for the Shi-Malik criterion (parameterized by k), and q = 9 for the Balanced Modularity criterion.

The C++ version is based on version 0.3 (https://sourceforge.net/projects/louvain/). Binary files are required to run it, and 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 or test the binary files, PLEASE MAKE SURE to set check_install accordingly.

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

Note

Although this algorithm was not primarily designed to deal with bipartite networks, it is possible to consider the bipartite network as a unipartite network (bipartite = TRUE).

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.

References

Blondel VD, Guillaume JL, Lambiotte R & Mech ELJS (2008) Fast unfolding of communities in large networks. J. Stat. Mech. 10, P10008.

See also

For more details illustrated with a practical example, see the vignette: https://biorgeo.github.io/bioregion/articles/a4_3_network_clustering.html.

Associated functions: netclu_infomap netclu_greedy netclu_oslom

Author

Maxime Lenormand (maxime.lenormand@inrae.fr)
Pierre Denelle (pierre.denelle@gmail.com)
Boris Leroy (leroy.boris@gmail.com)

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_louvain(net, lang = "igraph")