Bipartite random graphs

Generate bipartite graphs using the Erdős-Rényi model. Use sample_bipartite_gnm() and sample_bipartite_gnp() instead.

Usage

sample_bipartite(
  n1,
  n2,
  type = c("gnp", "gnm"),
  p,
  m,
  directed = FALSE,
  mode = c("out", "in", "all")
)

bipartite(..., type = NULL)

Arguments

n1

Integer scalar, the number of bottom vertices.

n2

Integer scalar, the number of top vertices.

type

Character scalar, the type of the graph, ‘gnp’ creates a \(G(n,p)\) graph, ‘gnm’ creates a \(G(n,m)\) graph. See details below.

p

Real scalar, connection probability for \(G(n,p)\) graphs. Should not be given for \(G(n,m)\) graphs.

m

Integer scalar, the number of edges for \(G(n,m)\) graphs. Should not be given for \(G(n,p)\) 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.

...

Passed to sample_bipartite().

Value

A bipartite igraph graph.

See also

Random graph models (games) bipartite_gnm(), erdos.renyi.game(), sample_(), 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()

Author

Gabor Csardi csardi.gabor@gmail.com

Examples

## empty graph
sample_bipartite(10, 5, p = 0)
#> Warning: `sample_bipartite()` was deprecated in igraph 2.2.0.
#> ℹ Please use `sample_bipartite_gnp()` instead.
#> IGRAPH 41b78d7 U--B 15 0 -- Bipartite Gnp random graph
#> + attr: name (g/c), p (g/n), type (v/l)
#> + edges from 41b78d7:

## full graph
sample_bipartite(10, 5, p = 1)
#> IGRAPH 5cf2dc1 U--B 15 50 -- Bipartite Gnp random graph
#> + attr: name (g/c), p (g/n), type (v/l)
#> + edges from 5cf2dc1:
#>  [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(10, 5, p = .1)
#> IGRAPH 9b7631b U--B 15 8 -- Bipartite Gnp random graph
#> + attr: name (g/c), p (g/n), type (v/l)
#> + edges from 9b7631b:
#> [1] 4--12 5--12 1--13 2--13 7--13 2--14 3--14 5--15

## directed bipartite graph, G(n,m)
sample_bipartite(10, 5, type = "Gnm", m = 20, directed = TRUE, mode = "all")
#> Warning: `sample_bipartite()` was deprecated in igraph 2.2.0.
#> ℹ Please use `sample_bipartite_gnm()` instead.
#> IGRAPH 0786f1c D--B 15 20 -- Bipartite Gnm random graph
#> + attr: name (g/c), m (g/n), type (v/l)
#> + edges from 0786f1c:
#>  [1]  1->11  3->11  5->11  6->11  1->13  4->13  7->13  8->13  4->14  3->15
#> [11]  4->15 10->15 12-> 1 13-> 3 11-> 4 12-> 6 13-> 6 14-> 6 15-> 8 14->10