Generate random graphs according to the \(G(n,m)\) Erdős-Rényi model

Random graph with a fixed number of edges and vertices.

Usage

sample_gnm(n, m, directed = FALSE, loops = FALSE)

gnm(n, m, directed = FALSE, loops = FALSE)

Arguments

n: The number of vertices in the graph.
m: The number of edges in the graph.
directed: Logical, whether the graph will be directed, defaults to FALSE.
loops: Logical, whether to add loop edges, defaults to FALSE.

Value

A graph object.

Details

The graph has n vertices and m edges. The edges are chosen uniformly at random from the set of all vertex pairs. This set includes potential self-connections as well if the loops parameter is TRUE.

References

Erdős, P. and Rényi, A., On random graphs, Publicationes Mathematicae 6, 290–297 (1959).

Random graph models (games) bipartite_gnm(), 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_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 <- sample_gnm(1000, 1000)
degree_distribution(g)
#> [1] 0.127 0.279 0.258 0.200 0.087 0.039 0.008 0.002