netclu_beckett {bioregion} | R Documentation |
Community structure detection in weighted bipartite network via modularity optimization
Description
This function takes a bipartite weighted graph and computes modules by applying Newman’s modularity measure in a bipartite weighted version to it.
Usage
netclu_beckett(
net,
weight = TRUE,
index = names(net)[3],
site_col = 1,
species_col = 2,
return_node_type = "both",
forceLPA = FALSE,
algorithm_in_output = TRUE
)
Arguments
net |
a |
weight |
a |
index |
name or number of the column to use as weight. By default,
the third column name of |
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 |
forceLPA |
a |
algorithm_in_output |
a |
Details
This function is based on the modularity optimization algorithm provided by Stephen Beckett (Beckett 2016) as implemented in the bipartite package (computeModules).
Value
A list
of class bioregion.clusters
with five slots:
name:
character string
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 an
object of class "moduleWeb", output of
computeModules.
Note
Beckett has been designed to deal with weighted bipartite networks. Note
that if weight = FALSE
, a weight of 1 will be assigned to each pair of
nodes. 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.
Author(s)
Maxime Lenormand (maxime.lenormand@inrae.fr), Pierre Denelle (pierre.denelle@gmail.com) and Boris Leroy (leroy.boris@gmail.com)
References
Beckett SJ (2016). “Improved community detection in weighted bipartite networks.” Royal Society Open Science, 3(1), 140536.
See Also
Examples
net <- data.frame(
Site = c(rep("A", 2), rep("B", 3), rep("C", 2)),
Species = c("a", "b", "a", "c", "d", "b", "d"),
Weight = c(10, 100, 1, 20, 50, 10, 20))
com <- netclu_beckett(net)