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Compute Centrality Measures for a Network

Source: R/centrality_measures.R
centrality.Rd

Computes centrality measures from a netobject or netobject_group. For directed networks the default measures are InStrength, OutStrength, and Betweenness. For undirected networks the defaults are Strength (column sums) and Betweenness.

Usage

centrality(x, ...)

# S3 method for class 'netobject'
centrality(x, measures = NULL, loops = FALSE, centrality_fn = NULL, ...)

# S3 method for class 'netobject_group'
centrality(x, measures = NULL, loops = FALSE, centrality_fn = NULL, ...)

# S3 method for class 'cograph_network'
centrality(x, measures = NULL, loops = FALSE, centrality_fn = NULL, ...)

# S3 method for class 'mcml'
centrality(x, measures = NULL, loops = FALSE, centrality_fn = NULL, ...)

Arguments

x

A netobject or netobject_group.

...

Additional arguments (ignored).

measures

Character vector. Centrality measures to compute. Built-in: "InStrength", "OutStrength", "Betweenness", "InCloseness", "OutCloseness", "Closeness". Default depends on directedness.

loops

Logical. Include self-loops (diagonal) in computation? Default: FALSE.

centrality_fn

Optional function. Custom centrality function that takes a weight matrix and returns a named list of centrality vectors.

Value

For a netobject: a data frame with node names as rows and centrality measures as columns. For a netobject_group: a named list of such data frames (one per group).

Examples

# \donttest{
seqs <- data.frame(
  V1 = c("A","B","A","C"), V2 = c("B","C","B","A"),
  V3 = c("C","A","C","B"))
net <- build_network(seqs, method = "relative")
centrality(net)
#>   InStrength OutStrength Betweenness
#> A          1           1           2
#> B          1           1           2
#> C          1           1           2
# }