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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.

## Usage

sample_gnp(n, p, directed = FALSE, loops = FALSE)

gnp(...)

## Arguments

n

The number of vertices in the graph.

p

The probability for drawing an edge between two arbitrary vertices ($$G(n,p)$$ graph).

directed

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

loops

Logical, whether to add loop edges, defaults to FALSE.

...

Passed to sample_gnp().

A graph object.

## 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.

## References

Erdős, P. and Rényi, A., On random graphs, Publicationes Mathematicae 6, 290–297 (1959).

## 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