compute_graph_modul: Compute modules from a graph by maximising modularity

In graph4lg: Build Graphs for Landscape Genetics Analysis

Compute modules from a graph by maximising modularity

Description

The function computes modules from a graph by maximising modularity.

Usage

compute_graph_modul(
  graph,
  algo = "fast_greedy",
  node_inter = NULL,
  nb_modul = NULL
)

Arguments

graph	An object of class igraph. Its nodes must have names.
algo	A character string indicating the algorithm used to create the modules with igraph. If algo = 'fast_greedy' (default), function cluster_fast_greedy from igraph is used (Clauset et al., 2004). If algo = 'walktrap', function cluster_walktrap from igraph is used (Pons et Latapy, 2006) with 4 steps (default options). If algo = 'louvain', function cluster_louvain from igraph is used (Blondel et al., 2008). In that case, the number of modules created in each graph is imposed. If algo = 'optimal', function cluster_optimal from igraph is used (Brandes et al., 2008) (can be very long). In that case, the number of modules created in each graph is imposed.
node_inter	(optional, default = NULL) A character string indicating whether the links of the graph are weighted by distances or by similarity indices. It is only used to compute the modularity index. It can be: 'distance': Link weights correspond to distances. Nodes that are close to each other will more likely be in the same module. 'similarity': Link weights correspond to similarity indices. Nodes that are similar to each other will more likely be in the same module. Inverse link weights are then used to compute the modularity index.
nb_modul	(optional , default = NULL) A numeric or integer value indicating the number of modules in the graph. When this number is not specified, the optimal value is retained.

Value

A data.frame with the node names and the corresponding module ID.

Author(s)

P. Savary

Examples

data("data_tuto")
mat_gen <- data_tuto[[1]]
graph <- gen_graph_thr(mat_w = mat_gen, mat_thr = mat_gen,
                            thr = 0.8)
res_mod <- compute_graph_modul(graph = graph,
                                algo = "fast_greedy",
                                node_inter = "distance")

graph4lg documentation built on Feb. 16, 2023, 5:43 p.m.