Bipartite random graphs

Usage

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

bipartite(...)

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.

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.

Random graph models (games) erdos.renyi.game(), sample_correlated_gnp_pair(), sample_correlated_gnp(), sample_degseq(), sample_dot_product(), sample_fitness_pl(), sample_fitness(), sample_forestfire(), sample_gnm(), sample_gnp(), sample_grg(), sample_growing(), sample_hierarchical_sbm(), sample_islands(), sample_k_regular(), sample_last_cit(), sample_pa_age(), sample_pa(), sample_pref(), sample_sbm(), sample_smallworld(), sample_traits_callaway(), sample_tree(), sample_()

Author

Gabor Csardi csardi.gabor@gmail.com

Examples


## empty graph
sample_bipartite(10, 5, p = 0)
#> IGRAPH 0948dd2 U--B 15 0 -- Bipartite Gnp random graph
#> + attr: name (g/c), p (g/n), type (v/l)
#> + edges from 0948dd2:

## full graph
sample_bipartite(10, 5, p = 1)
#> IGRAPH 8752e4a U--B 15 50 -- Bipartite Gnp random graph
#> + attr: name (g/c), p (g/n), type (v/l)
#> + edges from 8752e4a:
#>  [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 e434f77 U--B 15 5 -- Bipartite Gnp random graph
#> + attr: name (g/c), p (g/n), type (v/l)
#> + edges from e434f77:
#> [1] 5--11 1--14 8--14 9--14 1--15

## directed bipartite graph, G(n,m)
sample_bipartite(10, 5, type = "Gnm", m = 20, directed = TRUE, mode = "all")
#> IGRAPH 6588e2e D--B 15 20 -- Bipartite Gnm random graph
#> + attr: name (g/c), m (g/n), type (v/l)
#> + edges from 6588e2e:
#>  [1]  5->11  7->11  9->11  6->12  1->14  3->14  6->14  7->14  2->15  6->15
#> [11]  9->15 12-> 4 15-> 4 11-> 6 14-> 6 15-> 6 13-> 7 11-> 9 14-> 9 14->10