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.
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()
.
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, pmmax, where mmax
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_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()
Random graph models (games)
erdos.renyi.game()
,
sample_()
,
sample_bipartite()
,
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] 0.381 0.355 0.181 0.064 0.015 0.003 0.001