netclu_leiden: Finding communities using the Leiden algorithm

In bioregion: Comparison of Bioregionalisation Methods

Finding communities using the Leiden algorithm

Description

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 (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 random number generator seed (NULL for random by default).
objective_function	A string indicating the objective function to use, either the Constant Potts Model ("CPM") or "modularity" ("CPM" by default).
resolution_parameter	The resolution parameter to use. Higher resolutions lead to smaller communities, while lower resolutions lead to larger communities.
beta	A parameter affecting the randomness in the Leiden algorithm. This affects only the refinement step of the algorithm.
n_iterations	The number of iterations for the Leiden algorithm. Each iteration may further improve the partition.
vertex_weights	The vertex weights used in the Leiden algorithm. If not provided, they will be automatically determined based on the objective_function. Please see the details of this function to understand how to interpret the vertex weights.
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).
algorithm_in_output	A boolean indicating if the original output of cluster_leiden should be returned in the output (TRUE by default, see Value).

Details

This function is based on the Leiden algorithm (Traag et al., 2019) as implemented in the igraph package (cluster_leiden).

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_leiden.

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.

Author(s)

Maxime Lenormand ([e-mail])
Pierre Denelle ([e-mail])
Boris Leroy ([e-mail])

References

Traag VA, Waltman L & Van Eck NJ (2019) From Louvain to Leiden: guaranteeing well-connected communities. Scientific reports 9, 5233.

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_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_leiden(net)

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

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