sample_gnp | R Documentation |
G(n,p)
Erdős-Rényi modelEvery 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.
sample_gnp(n, p, directed = FALSE, loops = FALSE)
gnp(...)
n |
The number of vertices in the graph. |
p |
The probability for drawing an edge between two
arbitrary vertices ( |
directed |
Logical, whether the graph will be directed, defaults to
|
loops |
Logical, whether to add loop edges, defaults to |
... |
Passed to |
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.
A graph object.
Gabor Csardi csardi.gabor@gmail.com
Erdős, P. and Rényi, A., On random graphs, Publicationes Mathematicae 6, 290–297 (1959).
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()
# Random graph with expected mean degree of 2
g <- sample_gnp(1000, 2 / 1000)
mean(degree(g))
degree_distribution(g)
# Pick a simple graph on 6 vertices uniformly at random
plot(sample_gnp(6, 0.5))
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