erdos.renyi.game | R Documentation |

This model is very simple, every possible edge is created with the same constant probability.

```
erdos.renyi.game(
n,
p.or.m,
type = c("gnp", "gnm"),
directed = FALSE,
loops = FALSE
)
```

`n` |
The number of vertices in the graph. |

`p.or.m` |
Either the probability for drawing an edge between two
arbitrary vertices ( |

`type` |
The type of the random graph to create, either |

`directed` |
Logical, whether the graph will be directed, defaults to FALSE. |

`loops` |
Logical, whether to add loop edges, defaults to FALSE. |

In `G(n,p)`

graphs, the graph has ‘n’ vertices and for each edge the
probability that it is present in the graph is ‘p’.

In `G(n,m)`

graphs, the graph has ‘n’ vertices and ‘m’ edges,
and the ‘m’ edges are chosen uniformly randomly from the set of all
possible edges. This set includes loop edges as well if the `loops`

parameter is TRUE.

`random.graph.game()`

is an alias to this function.

A graph object.

Since igraph version 0.8.0, both `erdos.renyi.game()`

and
`random.graph.game()`

are deprecated, and `sample_gnp()`

and
`sample_gnm()`

should be used instead.

Gabor Csardi csardi.gabor@gmail.com

Erdos, P. and Renyi, A., On random graphs, *Publicationes
Mathematicae* 6, 290–297 (1959).

`sample_pa()`

Random graph models (games)
`sample_bipartite()`

,
`sample_correlated_gnp_pair()`

,
`sample_correlated_gnp()`

,
`sample_degseq()`

,
`sample_dot_product()`

,
`sample_fitness_pl()`

,
`sample_fitness()`

,
`sample_forestfire()`

,
`sample_gnm()`

,
`sample_gnp()`

,
`sample_grg()`

,
`sample_growing()`

,
`sample_hierarchical_sbm()`

,
`sample_islands()`

,
`sample_k_regular()`

,
`sample_last_cit()`

,
`sample_pa_age()`

,
`sample_pa()`

,
`sample_pref()`

,
`sample_sbm()`

,
`sample_smallworld()`

,
`sample_traits_callaway()`

,
`sample_tree()`

,
`sample_()`

```
g <- erdos.renyi.game(1000, 1 / 1000)
degree_distribution(g)
```

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