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