netclu_labelprop {bioregion}

R Documentation

Finding communities based on propagating labels

Description

This function finds communities in a (un)weighted undirected network based on propagating labels.

Usage

netclu_labelprop(
  net,
  weight = TRUE,
  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.
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 ("sites", "species" or "both") should be returned in the output (keep_nodes_type="both" by default).
algorithm_in_output	a boolean indicating if the original output of communities should be returned in the output (see Value).

Details

This function is based on propagating labels (Raghavan et al. 2007) as implemented in the igraph package (cluster_label_prop).

Value

A list of class bioregion.clusters with five slots:

1. name: ⁠character string⁠ 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 an "communities" object, output of cluster_label_prop.

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.

Author(s)

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

References

Raghavan UN, Albert R, Kumara S (2007). “Near linear time algorithm to detect community structures in large-scale networks.” Physical Review E, 76(3), 036106.

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_labelprop(net)

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

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