Generate random graphs according to the \(G(n,p)\) Erdős-Rényi model
Source:R/games.R
sample_gnp.RdEvery 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, \(p m_\text{max}\), 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_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] 0.329 0.371 0.203 0.079 0.013 0.004 0.001