Measures of network centralisation

Source: R/measure_centrality.R

Measures of network centralisation

network_degree(object, normalized = TRUE, direction = c("all", "out", "in"))

network_closeness(object, normalized = TRUE, direction = c("all", "out", "in"))

network_betweenness(
  object,
  normalized = TRUE,
  direction = c("all", "out", "in")
)

network_eigenvector(object, normalized = TRUE)

Arguments

object

An object of a migraph-consistent class:

  • matrix (adjacency or incidence) from {base} R

  • edgelist, a data frame from {base} R or tibble from {tibble}

  • igraph, from the {igraph} package

  • network, from the {network} package

  • tbl_graph, from the {tidygraph} package

normalized

Logical scalar, whether the centrality scores are normalized. Different denominators are used depending on whether the object is one-mode or two-mode, the type of centrality, and other arguments.

direction

Character string, “out” bases the measure on outgoing ties, “in” on incoming ties, and "all" on either/the sum of the two. For two-mode networks, "all" uses as numerator the sum of differences between the maximum centrality score for the mode against all other centrality scores in the network, whereas "in" uses as numerator the sum of differences between the maximum centrality score for the mode against only the centrality scores of the other nodes in that mode.

Functions

  • network_degree(): Calculate the degree centralization for a graph

  • network_closeness(): Calculate the closeness centralization for a graph

  • network_betweenness(): Calculate the betweenness centralization for a graph

  • network_eigenvector(): Calculate the eigenvector centralization for a graph

See also

Other measures: centrality, closure, cohesion(), diversity, features, holes, tie_centrality

Examples

network_degree(ison_southern_women, direction = "in")
#> Mode 1 Mode 2 
#>  0.231  0.466 
network_closeness(ison_southern_women, direction = "in")
#> Mode 1 Mode 2 
#>  0.214  0.528 
network_betweenness(ison_southern_women, direction = "in")
#> Mode 1 Mode 2 
#> 0.0668 0.1982 
network_eigenvector(mpn_elite_mex)
#> [1] 0.63
network_eigenvector(ison_southern_women)
#> Mode 1 Mode 2 
#> 0.0849 0.2630