Measures of betweenness-like centrality and centralisation

Measures of betweenness-like centrality and centralisation

node_betweenness(.data, normalized = TRUE, cutoff = NULL)

tie_betweenness(.data, normalized = TRUE)

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

Arguments

.data

An object of a {manynet}-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.

cutoff

The maximum path length to consider when calculating betweenness. If negative or NULL (the default), there's no limit to the path lengths considered.

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.

Value

A numeric vector giving the betweenness centrality measure of each node.

Functions

• node_betweenness(): Calculate the betweenness centralities of nodes in a network

• tie_betweenness(): Calculate number of shortest paths going through a tie

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

See also

Other measures: close_centrality, closure, cohesion(), degree_centrality, diffusion, eigenv_centrality, features, heterogeneity, hierarchy, holes

Other centrality: close_centrality, degree_centrality, eigenv_centrality

Examples

node_betweenness(mpn_elite_mex)
#>   Trevino Madero Carranza Aguilar Obregon Calles `Aleman Gonzalez` `Portes Gil`
#> 1   0.005  0.008    0.031   0.021   0.008  0.025             0.009        0.039
#> # ... with 27 more values from this nodeset unprinted. Use `print(..., n = Inf)` to print all values.
node_betweenness(ison_southern_women)
#>   EVELYN LAURA THERESA BRENDA CHARLOTTE FRANCES ELEANOR PEARL  RUTH VERNE  MYRA
#> 1  0.097 0.052   0.088   0.05     0.011   0.011   0.009 0.007 0.017 0.014 0.013
#> # ... with 7 more values from this nodeset unprinted. Use `print(..., n = Inf)` to print all values.
#>      E1    E2    E3    E4    E5    E6    E7    E8    E9   E10   E11   E12   E13
#> 1 0.002 0.002 0.018 0.008 0.038 0.062 0.129  0.24 0.213 0.015  0.02 0.023 0.004
#> # ... with 1 more values from this nodeset unprinted. Use `print(..., n = Inf)` to print all values.
(tb <- tie_betweenness(ison_adolescents))
#>   `Betty-Sue` `Sue-Alice` `Alice-Jane` `Sue-Dale` `Alice-Dale` `Jane-Dale`
#> 1           7           3            5        4.5          2.5           2
#> # ... with 4 more values from this nodeset unprinted. Use `print(..., n = Inf)` to print all values.
plot(tb)

#ison_adolescents %>% mutate_ties(weight = tb) %>% 
#   autographr()
network_betweenness(ison_southern_women, direction = "in")
#> Mode 1 Mode 2 
#> 0.0668 0.1982