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:

**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.

## 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)
```