netclu_beckett {bioregion}

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,
  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 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 computeModules should be returned in the output (TRUE by default, see Value).

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:

1. name: character containing the name of the algorithm

2. args: list of input arguments as provided by the user

3. inputs: list of characteristics of the clustering process

4. algorithm: list of all objects associated with the clustering procedure, such as original cluster objects (only if algorithm_in_output = TRUE)

5. clusters: data.frame containing the clustering results

In the algorithm slot, if algorithm_in_output = TRUE, users can find the 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 ([e-mail]), Pierre Denelle ([e-mail]) and Boris Leroy ([e-mail])

References

Beckett SJ (2016). “Improved community detection in weighted bipartite networks.” Royal Society Open Science, 3(1), 140536.

See Also

netclu_infomap, netclu_oslom

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)