Measures of tie centrality

Source: R/measure_centrality.R

Measures of tie centrality

tie_degree(object, normalized = TRUE)

tie_closeness(object, normalized = TRUE)

tie_betweenness(object, normalized = TRUE)

tie_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.

Functions

  • tie_degree(): Calculate the degree centrality of edges in a network

  • tie_closeness(): Calculate the closeness of each edge to each other edge in the network.

  • tie_betweenness(): Calculate number of shortest paths going through an edge

  • tie_eigenvector(): Calculate the eigenvector centrality of edges in a network

See also

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

Examples

tie_degree(ison_adolescents)
#>   `Betty-Sue` Sue-Alic…¹ Alice…² Sue-D…³ Alice…⁴ Jane-…⁵ Sue-P…⁶ Alice…⁷ Pam-C…⁸
#> 1       0.333      0.667   0.444   0.556   0.556   0.333   0.556   0.556   0.333
#> # ... with 1 more from this nodeset in the vector.
(ec <- tie_closeness(ison_adolescents))
#>   `Betty-Sue` Sue-Alic…¹ Alice…² Sue-D…³ Alice…⁴ Jane-…⁵ Sue-P…⁶ Alice…⁷ Pam-C…⁸
#> 1       0.562      0.692     0.6   0.643   0.643     0.5   0.692   0.692   0.562
#> # ... with 1 more from this nodeset in the vector.
plot(ec)

ison_adolescents %>% 
  activate(edges) %>% mutate(weight = ec) %>% 
  autographr()

(tb <- tie_betweenness(ison_adolescents))
#>   `Betty-Sue` Sue-Alic…¹ Alice…² Sue-D…³ Alice…⁴ Jane-…⁵ Sue-P…⁶ Alice…⁷ Pam-C…⁸
#> 1           7          3       5     4.5     2.5       2     7.5     7.5      12
#> # ... with 1 more from this nodeset in the vector.
plot(tb)

ison_adolescents %>% 
  activate(edges) %>% mutate(weight = tb) %>% 
  autographr()

tie_eigenvector(ison_adolescents)
#>   `Betty-Sue` Sue-Alic…¹ Alice…² Sue-D…³ Alice…⁴ Jane-…⁵ Sue-P…⁶ Alice…⁷ Pam-C…⁸
#> 1       0.366      0.638   0.447   0.524   0.541   0.333   0.502   0.520   0.236
#> # ... with 1 more from this nodeset in the vector.