See `centralize()`

for a summary of graph centralization.

## Usage

```
centr_degree(
graph,
mode = c("all", "out", "in", "total"),
loops = TRUE,
normalized = TRUE
)
```

## Arguments

- graph
The input graph.

- mode
This is the same as the

`mode`

argument of`degree()`

.- loops
Logical scalar, whether to consider loops edges when calculating the degree.

- normalized
Logical scalar. Whether to normalize the graph level centrality score by dividing by the theoretical maximum.

## Value

A named list with the following components:

- res
The node-level centrality scores.

- centralization
The graph level centrality index.

- theoretical_max
The maximum theoretical graph level centralization score for a graph with the given number of vertices, using the same parameters. If the

`normalized`

argument was`TRUE`

, then the result was divided by this number.

## See also

Other centralization related:
`centr_betw_tmax()`

,
`centr_betw()`

,
`centr_clo_tmax()`

,
`centr_clo()`

,
`centr_degree_tmax()`

,
`centr_eigen_tmax()`

,
`centr_eigen()`

,
`centralize()`

## Examples

```
# A BA graph is quite centralized
g <- sample_pa(1000, m = 4)
centr_degree(g)$centralization
#> [1] 0.1638375
centr_clo(g, mode = "all")$centralization
#> [1] 0.4236249
centr_betw(g, directed = FALSE)$centralization
#> [1] 0.2452183
centr_eigen(g, directed = FALSE)$centralization
#> [1] 0.941744
```