Bipartite random graphs

Generate bipartite graphs using the Erdős-Rényi model

bipartite_gnm(...)

bipartite_gnp(...)

sample_bipartite_gnm(
  n1,
  n2,
  m,
  ...,
  directed = FALSE,
  mode = c("out", "in", "all")
)

sample_bipartite_gnp(
  n1,
  n2,
  p,
  ...,
  directed = FALSE,
  mode = c("out", "in", "all")
)

Arguments

...

Passed to sample_bipartite_gnp().

n1

Integer scalar, the number of bottom vertices.

n2

Integer scalar, the number of top vertices.

m

Integer scalar, the number of edges for \(G(n,m)\) graphs.

directed

Logical scalar, whether to create a directed graph. See also the mode argument.

mode

Character scalar, specifies how to direct the edges in directed graphs. If it is ‘out’, then directed edges point from bottom vertices to top vertices. If it is ‘in’, edges point from top vertices to bottom vertices. ‘out’ and ‘in’ do not generate mutual edges. If this argument is ‘all’, then each edge direction is considered independently and mutual edges might be generated. This argument is ignored for undirected graphs.

p

Real scalar, connection probability for \(G(n,p)\) graphs.

Details

Similarly to unipartite (one-mode) networks, we can define the \(G(n,p)\), and \(G(n,m)\) graph classes for bipartite graphs, via their generating process. In \(G(n,p)\) every possible edge between top and bottom vertices is realized with probability \(p\), independently of the rest of the edges. In \(G(n,m)\), we uniformly choose \(m\) edges to realize.

See also

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_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()

Examples

## empty graph
sample_bipartite_gnp(10, 5, p = 0)
#> IGRAPH abbc614 U--B 15 0 -- Bipartite Gnp random graph
#> + attr: name (g/c), p (g/n), type (v/l)
#> + edges from abbc614:

## full graph
sample_bipartite_gnp(10, 5, p = 1)
#> IGRAPH 422b4a8 U--B 15 50 -- Bipartite Gnp random graph
#> + attr: name (g/c), p (g/n), type (v/l)
#> + edges from 422b4a8:
#>  [1]  1--11  1--12  1--13  1--14  1--15  2--11  2--12  2--13  2--14  2--15
#> [11]  3--11  3--12  3--13  3--14  3--15  4--11  4--12  4--13  4--14  4--15
#> [21]  5--11  5--12  5--13  5--14  5--15  6--11  6--12  6--13  6--14  6--15
#> [31]  7--11  7--12  7--13  7--14  7--15  8--11  8--12  8--13  8--14  8--15
#> [41]  9--11  9--12  9--13  9--14  9--15 10--11 10--12 10--13 10--14 10--15

## random bipartite graph
sample_bipartite_gnp(10, 5, p = .1)
#> IGRAPH 6654364 U--B 15 2 -- Bipartite Gnp random graph
#> + attr: name (g/c), p (g/n), type (v/l)
#> + edges from 6654364:
#> [1] 1--13 3--14

## directed bipartite graph, G(n,m)
sample_bipartite_gnm(10, 5, m = 20, directed = TRUE, mode = "all")
#> IGRAPH ef09c98 D--B 15 20 -- Bipartite Gnm random graph
#> + attr: name (g/c), m (g/n), type (v/l)
#> + edges from ef09c98:
#>  [1]  4->11  9->11  3->13 10->13  2->14  5->14  6->14 10->14  3->15  7->15
#> [11] 13-> 3 15-> 3 12-> 4 15-> 5 11-> 6 12-> 6 11-> 7 15-> 7 11-> 9 12-> 9