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

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.
#>   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)

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

#>   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)

#>   Betty-Sue Sue-Alic…¹ Alice…² Sue-D…³ Alice…⁴ Jane-…⁵ Sue-P…⁶ Alice…⁷ Pam-C…⁸