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()
ordissimilarity_to_similarity()
. If adata.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:
name:
character
containing the name of the algorithmargs:
list
of input arguments as provided by the userinputs:
list
of characteristics of the clustering processalgorithm:
list
of all objects associated with the clustering procedure, such as original cluster objects (only ifalgorithm_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.
Details
This function is based on the Leiden algorithm (Traag et al. 2019) 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
Traag VA, Waltman L, Van Eck NJ (2019). “From Louvain to Leiden: guaranteeing well-connected communities.” Scientific reports, 9(1), 5233. Publisher: Nature Publishing Group UK London.
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)