This function finds communities in a (un)weighted undirected network based on the Leiden algorithm of Traag, van Eck & Waltman.
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
)A list of class bioregion.clusters with five slots:
name: character containing the name of the algorithm
args: list of input arguments as provided by the user
inputs: list of characteristics of the clustering process
algorithm: list of all objects associated with the
clustering procedure, such as original cluster objects (only if
algorithm_in_output = TRUE)
clusters: data.frame containing the clustering results
In the algorithm slot, if algorithm_in_output = TRUE, users can
find the output of
cluster_leiden.
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).
a string indicating the objective function to use, the Constant Potts Model ("CPM") or "modularity" ("CPM" by default).
the resolution parameter to use. Higher resolutions lead to more smaller communities, while lower resolutions lead to fewer larger communities.
parameter affecting the randomness in the Leiden algorithm. This affects only the refinement step of the algorithm.
the number of iterations to iterate the Leiden algorithm. Each iteration may improve the partition further.
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.
a boolean indicating if the network is bipartite
(see Details).
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
("sites", "species" or "both") should be returned in the output
(return_node_type = "both" by default).
a boolean indicating if the original output
of cluster_leiden should be returned in the
output (TRUE by default, see Value).
Maxime Lenormand (maxime.lenormand@inrae.fr), Pierre Denelle (pierre.denelle@gmail.com) and Boris Leroy (leroy.boris@gmail.com)
This function is based on the Leiden algorithm Traag2019bioregion as implemented in the igraph package (cluster_leiden).
Traag2019bioregion
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)
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