netclu_leadingeigen {bioregion} | R Documentation |
Finding communities based on the leading eigenvector of the community matrix
Description
This function finds communities in a (un)weighted undirected network based on the leading eigenvector of the community matrix.
Usage
netclu_leadingeigen(
net,
weight = TRUE,
cut_weight = 0,
index = names(net)[3],
bipartite = FALSE,
site_col = 1,
species_col = 2,
return_node_type = "both",
algorithm_in_output = TRUE
)
Arguments
net |
The output object from |
weight |
A |
cut_weight |
A minimal weight value. If |
index |
The name or number of the column to use as weight. By default,
the third column name of |
bipartite |
A |
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 |
algorithm_in_output |
A |
Details
This function is based on the leading eigenvector of the community matrix (Newman, 2006) as implemented in the igraph package (cluster_leading_eigen).
Value
A list
of class bioregion.clusters
with five slots:
name: A
character
containing the name of the algorithm.args: A
list
of input arguments as provided by the user.inputs: A
list
of characteristics of the clustering process.algorithm: A
list
of all objects associated with the clustering procedure, such as original cluster objects (only ifalgorithm_in_output = TRUE
).clusters: A
data.frame
containing the clustering results.
In the algorithm
slot, if algorithm_in_output = TRUE
, users can
find the output of cluster_leading_eigen.
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 (maxime.lenormand@inrae.fr)
Pierre Denelle (pierre.denelle@gmail.com)
Boris Leroy (leroy.boris@gmail.com)
References
Newman MEJ (2006) Finding community structure in networks using the eigenvectors of matrices. Physical Review E 74, 036104.
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_leadingeigen(net)
net_bip <- mat_to_net(comat, weight = TRUE)
clust2 <- netclu_leadingeigen(net_bip, bipartite = TRUE)