Generate random graphs according to the \(G(n,p)\) Erdős-Rényi model
Source:R/games.R
sample_gnp.Rd
This model is very simple, every possible edge is created with the same constant probability.
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 for each edge the probability that it is present in the graph is ‘p’.
References
Erdos, P. and Renyi, A., On random graphs, Publicationes Mathematicae 6, 290--297 (1959).
See also
Random graph models (games)
erdos.renyi.game()
,
sample_bipartite()
,
sample_correlated_gnp_pair()
,
sample_correlated_gnp()
,
sample_degseq()
,
sample_dot_product()
,
sample_fitness_pl()
,
sample_fitness()
,
sample_forestfire()
,
sample_gnm()
,
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_()
Random graph models (games)
erdos.renyi.game()
,
sample_bipartite()
,
sample_correlated_gnp_pair()
,
sample_correlated_gnp()
,
sample_degseq()
,
sample_dot_product()
,
sample_fitness_pl()
,
sample_fitness()
,
sample_forestfire()
,
sample_gnm()
,
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_()
Author
Gabor Csardi csardi.gabor@gmail.com
Examples
g <- sample_gnp(1000, 1 / 1000)
degree_distribution(g)
#> [1] 0.369 0.355 0.201 0.052 0.020 0.003