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Finding communities using the Leiden algorithm

Source: R/netclu_leiden.R
netclu_leiden.Rd

This function finds communities in a (un)weighted undirected network based on the Leiden algorithm of Traag, van Eck & Waltman.

Usage

netclu_leiden(
  net,
  weight = TRUE,
  cut_weight = 0,
  index = names(net)[3],
  seed = NULL,
  objective_function = "CPM",
  resolution_parameter = 1,
  beta = 0.01,
  n_iterations = 2,
  vertex_weights = NULL,
  bipartite = FALSE,
  site_col = 1,
  species_col = 2,
  return_node_type = "both",
  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 (O by default).

index

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

seed

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

objective_function

a string indicating the objective function to use, the Constant Potts Model ("CPM") or "modularity" ("CPM" by default).

resolution_parameter

the resolution parameter to use. Higher resolutions lead to more smaller communities, while lower resolutions lead to fewer larger communities.

beta

parameter affecting the randomness in the Leiden algorithm. This affects only the refinement step of the algorithm.

n_iterations

the number of iterations to iterate the Leiden algorithm. Each iteration may improve the partition further.

vertex_weights

the vertex weights used in the Leiden algorithm. If this is not provided, it will be automatically determined on the basis of the objective_function. Please see the details of this function how to interpret the vertex weights.

bipartite

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

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

algorithm_in_output

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

Value

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 (only if algorithm_in_output = TRUE)

  5. clusters: data.frame containing the clustering results

In the algorithm slot, if algorithm_in_output = TRUE, users can find the output of cluster_leiden.

Details

This function is based on the Leiden algorithm Traag2019bioregion as implemented in the igraph package (cluster_leiden).

Note

Although this algorithm was not primarily designed to deal with bipartite network, it is possible to consider the bipartite network as 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, "sites" to preserve only the sites nodes and "species" to preserve only the species nodes.

References

Traag2019bioregion

Author

Maxime Lenormand (maxime.lenormand@inrae.fr), Pierre Denelle (pierre.denelle@gmail.com) and 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_leiden(net)

net_bip <- mat_to_net(comat, weight = TRUE)
clust2 <- netclu_leiden(net_bip, bipartite = TRUE)