# Generate random graphs according to the \(G(n,p)\) Erdős-Rényi model

Source:`R/games.R`

`sample_gnp.Rd`

Every possible edge is created independently with the same probability `p`

.
This model is also referred to as a Bernoulli random graph since the
connectivity status of vertex pairs follows a Bernoulli distribution.

## Details

The graph has `n`

vertices and each pair of vertices is connected
with the same probability `p`

. The `loops`

parameter controls whether
self-connections are also considered. This model effectively constrains
the average number of edges, \(p m_\text{max}\), where \(m_\text{max}\)
is the largest possible number of edges, which depends on whether the
graph is directed or undirected and whether self-loops are allowed.

## See also

Random graph models (games)
`erdos.renyi.game()`

,
`sample_()`

,
`sample_bipartite()`

,
`sample_chung_lu()`

,
`sample_correlated_gnp()`

,
`sample_correlated_gnp_pair()`

,
`sample_degseq()`

,
`sample_dot_product()`

,
`sample_fitness()`

,
`sample_fitness_pl()`

,
`sample_forestfire()`

,
`sample_gnm()`

,
`sample_grg()`

,
`sample_growing()`

,
`sample_hierarchical_sbm()`

,
`sample_islands()`

,
`sample_k_regular()`

,
`sample_last_cit()`

,
`sample_pa()`

,
`sample_pa_age()`

,
`sample_pref()`

,
`sample_sbm()`

,
`sample_smallworld()`

,
`sample_traits_callaway()`

,
`sample_tree()`

## Author

Gabor Csardi csardi.gabor@gmail.com

## Examples

```
g <- sample_gnp(1000, 1 / 1000)
degree_distribution(g)
#> [1] 0.345 0.376 0.182 0.074 0.019 0.002 0.002
```