Generate bipartite graphs using the Erdos-Renyi model
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()
.
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_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_()
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 bce1742 U--B 15 0 -- Bipartite Gnp random graph
#> + attr: name (g/c), p (g/n), type (v/l)
#> + edges from bce1742:
## full graph
sample_bipartite(10, 5, p = 1)
#> IGRAPH 369311e U--B 15 50 -- Bipartite Gnp random graph
#> + attr: name (g/c), p (g/n), type (v/l)
#> + edges from 369311e:
#> [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 ef5ed37 U--B 15 6 -- Bipartite Gnp random graph
#> + attr: name (g/c), p (g/n), type (v/l)
#> + edges from ef5ed37:
#> [1] 7--12 5--13 2--15 4--15 6--15 8--15
## directed bipartite graph, G(n,m)
sample_bipartite(10, 5, type = "Gnm", m = 20, directed = TRUE, mode = "all")
#> IGRAPH 5a7dc2e D--B 15 20 -- Bipartite Gnm random graph
#> + attr: name (g/c), m (g/n), type (v/l)
#> + edges from 5a7dc2e:
#> [1] 2->11 8->11 10->11 1->12 7->12 4->13 7->13 6->14 4->15 10->15
#> [11] 15-> 1 12-> 2 13-> 2 15-> 2 11-> 3 12-> 3 13-> 4 15-> 6 12-> 7 15->10