Simple random graph model, specifying the edge count either precisely (G(n,m) model) or on average through a connection probability (G(n,p) model).
Usage
erdos.renyi.game(
n,
p.or.m,
type = c("gnp", "gnm"),
directed = FALSE,
loops = FALSE
)
Arguments
- n
The number of vertices in the graph.
- p.or.m
Either the probability for drawing an edge between two arbitrary vertices (G(n,p) graph), or the number of edges in the graph (for G(n,m) graphs).
- type
The type of the random graph to create, either
gnp()
(G(n,p) graph) orgnm()
(G(n,m) graph).- directed
Logical, whether the graph will be directed, defaults to
FALSE
.- loops
Logical, whether to add loop edges, defaults to
FALSE
.
Details
In G(n,m) graphs, there are precisely m
edges.
In G(n,p) graphs, all vertex pairs are connected with the same
probability p
.
random.graph.game()
is an alias to this function.
Deprecated
Since igraph version 0.8.0, both erdos.renyi.game()
and
random.graph.game()
are deprecated, and sample_gnp()
and
sample_gnm()
should be used instead. See these for more details.
References
Erdős, P. and Rényi, A., On random graphs, Publicationes Mathematicae 6, 290--297 (1959).
See also
Random graph models (games)
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_gnp()
,
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 <- erdos.renyi.game(1000, 1 / 1000)
degree_distribution(g)
#> [1] 0.357 0.355 0.204 0.070 0.013 0.001