`R/measure_centrality.R`

`between_centrality.Rd`

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")
)
```

- .data
An object of a

`{manynet}`

-consistent class:matrix (adjacency or incidence) from

`{base}`

Redgelist, a data frame from

`{base}`

R or tibble from`{tibble}`

igraph, from the

`{igraph}`

packagenetwork, from the

`{network}`

packagetbl_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.

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

`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

Other measures:
`close_centrality`

,
`closure`

,
`cohesion()`

,
`degree_centrality`

,
`diffusion`

,
`eigenv_centrality`

,
`features`

,
`heterogeneity`

,
`hierarchy`

,
`holes`

Other centrality:
`close_centrality`

,
`degree_centrality`

,
`eigenv_centrality`

```
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
```