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Community structure detection in weighted bipartite networks 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.

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 first two columns representing undirected links between pairs of nodes, and the next column(s) representing the weights of the links.

weight

A boolean indicating whether weights should be considered if there are more than two columns (see Note).

cut_weight

A minimal weight value. If weight is TRUE, links with weights strictly lower than this value will not be considered (0 by default).

index

The name or number of the column to use as weight. By default, the third column name of net is used.

seed

The seed for the random number generator (NULL for random by default).

forceLPA

A boolean indicating whether 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

The name or number of the column for site nodes (i.e., primary nodes).

species_col

The name or number of the column for species nodes (i.e., feature nodes).

return_node_type

A character indicating which types of nodes ("site", "species", or "both") should be returned in the output ("both" by default).

algorithm_in_output

A boolean indicating whether 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:

  1. name: A character containing the name of the algorithm.

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

  3. inputs: A list of characteristics of the clustering process.

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

  5. clusters: A data.frame containing the clustering results.

If algorithm_in_output = TRUE, users can find the output of computeModules in the algorithm slot.

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's algorithm is designed to handle weighted bipartite networks. If weight = FALSE, a weight of 1 will be assigned to each pair of nodes. Ensure that the site_col and species_col arguments correctly identify the respective columns for site nodes (primary nodes) and species nodes (feature nodes). The type of nodes returned in the output can be selected using the return_node_type argument: "both" to include both node types, "site" to return only site nodes, or "species" to return only species nodes.

References

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

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

Author

Maxime Lenormand (maxime.lenormand@inrae.fr)
Pierre Denelle (pierre.denelle@gmail.com)
Boris Leroy (leroy.boris@gmail.com)

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)