The degree of a vertex is its most basic structural property, the number of its adjacent edges.

## Arguments

- graph
The graph to analyze.

- v
The ids of vertices of which the degree will be calculated.

- mode
Character string, “out” for out-degree, “in” for in-degree or “total” for the sum of the two. For undirected graphs this argument is ignored. “all” is a synonym of “total”.

- loops
Logical; whether the loop edges are also counted.

- normalized
Logical scalar, whether to normalize the degree. If

`TRUE`

then the result is divided by \(n-1\), where \(n\) is the number of vertices in the graph.- cumulative
Logical; whether the cumulative degree distribution is to be calculated.

- ...
Additional arguments to pass to

`degree()`

, e.g.`mode`

is useful but also`v`

and`loops`

make sense.

## Value

For `degree()`

a numeric vector of the same length as argument
`v`

.

For `degree_distribution()`

a numeric vector of the same length as the
maximum degree plus one. The first element is the relative frequency zero
degree vertices, the second vertices with degree one, etc.

## See also

Other structural.properties:
`bfs()`

,
`component_distribution()`

,
`connect()`

,
`constraint()`

,
`coreness()`

,
`dfs()`

,
`distance_table()`

,
`edge_density()`

,
`feedback_arc_set()`

,
`girth()`

,
`is_dag()`

,
`is_matching()`

,
`knn()`

,
`laplacian_matrix()`

,
`reciprocity()`

,
`subcomponent()`

,
`subgraph()`

,
`topo_sort()`

,
`transitivity()`

,
`unfold_tree()`

,
`which_multiple()`

,
`which_mutual()`

Other structural.properties:
`bfs()`

,
`component_distribution()`

,
`connect()`

,
`constraint()`

,
`coreness()`

,
`dfs()`

,
`distance_table()`

,
`edge_density()`

,
`feedback_arc_set()`

,
`girth()`

,
`is_dag()`

,
`is_matching()`

,
`knn()`

,
`laplacian_matrix()`

,
`reciprocity()`

,
`subcomponent()`

,
`subgraph()`

,
`topo_sort()`

,
`transitivity()`

,
`unfold_tree()`

,
`which_multiple()`

,
`which_mutual()`

## Author

Gabor Csardi csardi.gabor@gmail.com

## Examples

```
g <- make_ring(10)
degree(g)
#> [1] 2 2 2 2 2 2 2 2 2 2
g2 <- sample_gnp(1000, 10 / 1000)
degree_distribution(g2)
#> [1] 0.001 0.000 0.002 0.005 0.026 0.038 0.058 0.101 0.118 0.117 0.118 0.113
#> [13] 0.098 0.081 0.057 0.027 0.019 0.010 0.004 0.004 0.000 0.002 0.001
```