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Detect and Visualize Network Modules (Communities)

Source: R/find_modules.R
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,
  weights = NULL,
  weight_type = c("strength", "distance"),
  ...
)

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.

weights

Optional edge weights: NULL (default) to ignore them, the name of an edge attribute, or a numeric vector of length igraph::ecount(graph). Community detection reads a weight as a strength, except method = "edge_betweenness", which needs costs and is given them. method = "fluid_communities" cannot use weights and warns. See netkit-weights.

weight_type

Either "strength" (default) or "distance". See netkit-weights.

...

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

Value

A list with the following components:

result

A tibble mapping each node to its module assignment.

module_table

Deprecated alias for result, kept for backward compatibility.

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.

Edge weights

Functions that can use edge weights take two arguments:

weights

NULL (the default) to ignore edge weights; the name of an edge attribute, such as "weight"; or a numeric vector with one value per edge, in igraph::E(graph) order.

weight_type

"strength" (the default) if a larger value means a more tightly connected pair – confidence scores, correlations, co-expression, read counts, interaction scores. "distance" if a larger value means further apart – costs, dissimilarities, reaction times.

Declaring which you have is not bookkeeping. igraph reads the weight attribute implicitly and gives it opposite meanings in different functions: a cost in igraph::betweenness(), igraph::distances(), igraph::diameter() and igraph::mean_distance(), but a strength in igraph::cluster_louvain() and the other community detection algorithms. Attaching a confidence score and letting that happen implicitly therefore inverts every path-based metric – a high-confidence interaction is treated as a long distance – while community detection reads the same numbers the way you intended.

netkit resolves weights and weight_type once per call and derives both a strength and a distance vector from them, so each metric receives the one it needs. A "strength" is converted to a distance by reciprocal (\(1/w\)); a "distance" is converted to a strength by reflection (\(\max(w) - w + \min(w)\)), which keeps a zero distance finite.

References

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

Examples

g <- igraph::sample_pa(80, power = 1.5, directed = FALSE)
res <- find_modules(g, method = "louvain", plot = FALSE)
res$n_modules
#> [1] 8
head(res$module_table)
#> # A tibble: 6 × 2
#>   node  module
#>   <chr>  <int>
#> 1 1          1
#> 2 2          2
#> 3 5          5
#> 4 6          6
#> 5 8          2
#> 6 9          6