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Detect and Visualize Network Modules (Communities)

find_modules.Rd

Identifies modules (communities) in a network using a variety of community detection algorithms from the igraph package. Optionally filters out small modules, visualizes the detected modules, and returns induced subgraphs for each module.

Usage

find_modules(
  graph,
  method = "louvain",
  min_size = 3,
  no.of.communities = NULL,
  return_subgraphs = FALSE,
  plot = TRUE,
  label = FALSE,
  ...
)

Arguments

graph

An igraph object representing the network to analyze or a data frame containing a symbolic edge list in the first two columns. Additional columns are considered as edge attributes.

method

Character. Community detection method. Options include: "louvain", "walktrap", "infomap", "edge_betweenness", "fluid_communities", "fast_greedy", "leading_eigen", "leiden", and "spinglass".

min_size

Integer. Minimum number of nodes required to retain a module. Modules smaller than this size are discarded. Default is 3.

no.of.communities

Integer. Required only when method = "fluid_communities". Specifies the number of communities to find.

return_subgraphs

Logical. If TRUE, returns a list of induced subgraphs for each detected module.

plot

Logical. If TRUE, generates a network plot colored by module.

label

Logical. If TRUE, displays node labels in the plot.

...

Additional parameters passed to the plot_Net() function for customizing the plot.

Value

A list with the following components:

module_table

A tibble mapping each node to its module assignment.

n_modules

The number of modules that meet the min_size threshold.

subgraphs

A named list of subgraphs for each module (only if return_subgraphs = TRUE).

method

The community detection method used.

graph

The input graph with assigned 'module' and 'color' as vertex attributes, if plot = TRUE.

If plot = TRUE, a network plot is displayed with nodes colored by module.

#' @details This function is a wrapper around several igraph community detection algorithms, including Louvain (cluster_louvain()), Walktrap, Infomap, Fast Greedy, and others. It simplifies their application and offers optional filtering, visualization via plot_Net(), and module subgraph extraction.

References

Csardi G, Nepusz T. The igraph software package for complex network research. InterJournal, Complex Systems. 2006;1695. https://igraph.org

Examples

if (FALSE) { # \dontrun{
library(igraph)
g <- sample_pa(100)
find_modules(g, method = "louvain", plot = TRUE)
} # }