Community structure detection via greedy optimization of modularity

This function finds communities in a (un)weighted undirected network via greedy optimization of modularity.

Usage

netclu_greedy(
  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 similarity() or dissimilarity_to_similarity(). If a data.frame is used, the first two columns represent pairs of sites (or any pair of nodes), and the next column(s) are the similarity indices.
weight: a boolean indicating if the weights should be considered if there are more than two columns.
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.
bipartite: a boolean indicating if the network is bipartite (see Details).
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 cluster_fast_greedy 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 algorithm
args: list of input arguments as provided by the user
inputs: list of characteristics of the clustering process
algorithm: list of all objects associated with the clustering procedure, such as original cluster objects (only if algorithm_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 cluster_fast_greedy.

Details

This function is based on the fast greedy modularity optimization algorithm (Clauset et al., 2004) as implemented in the igraph package (cluster_fast_greedy).

Note

Although this algorithm was not primarily designed to deal with bipartite network, it is possible to consider the bipartite network as 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, sites to preserve only the sites nodes and species to preserve only the species nodes.

References

Clauset A, Newman MEJ & Moore C (2004) Finding community structure in very large networks. Phys. Rev. E, 70, 066111.

Author

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

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_greedy(net)

net_bip <- mat_to_net(comat, weight = TRUE)
clust2 <- netclu_greedy(net_bip, bipartite = TRUE)