Community structure detection in weighted bipartite network via modularity optimization
Source:R/netclu_beckett.R
netclu_beckett.Rd
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,
cut_weight = 0,
index = names(net)[3],
seed = NULL,
forceLPA = FALSE,
site_col = 1,
species_col = 2,
return_node_type = "both",
algorithm_in_output = TRUE
)
Arguments
- net
a
data.frame
representing a bipartite network with the two first columns as undirected links between pair of nodes and and the next column(s) are the weight of the links.- weight
a
boolean
indicating if the weights should be considered if there are more than two columns (see Note).- 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).
- forceLPA
a
boolean
indicating if the even faster pure LPA-algorithm of Beckett should be used? DIRT-LPA, the default, is less likely to get trapped in a local minimum, but is slightly slower. Defaults to FALSE.- 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 (site
,species
orboth
) should be returned in the output (return_node_type = "both"
by default).- algorithm_in_output
a
boolean
indicating if the original output of computeModules 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 computeModules.
Details
This function is based on the modularity optimization algorithm provided by Stephen Beckett (Beckett 2016) as implemented in the bipartite package (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.
References
Beckett SJ (2016). “Improved community detection in weighted bipartite networks.” Royal Society Open Science, 3(1), 140536.
Author
Maxime Lenormand (maxime.lenormand@inrae.fr), Pierre Denelle (pierre.denelle@gmail.com) and Boris Leroy (leroy.boris@gmail.com)