Generate bipartite graphs using the Erdős-Rényi model.
Use
sample_bipartite_gnm()
and sample_bipartite_gnp()
instead.
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()
.
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 8ec1d89 U--B 15 0 -- Bipartite Gnp random graph
#> + attr: name (g/c), p (g/n), type (v/l)
#> + edges from 8ec1d89:
## full graph
sample_bipartite(10, 5, p = 1)
#> IGRAPH 4ec0db4 U--B 15 50 -- Bipartite Gnp random graph
#> + attr: name (g/c), p (g/n), type (v/l)
#> + edges from 4ec0db4:
#> [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 3482901 U--B 15 6 -- Bipartite Gnp random graph
#> + attr: name (g/c), p (g/n), type (v/l)
#> + edges from 3482901:
#> [1] 2--11 7--11 1--14 9--14 5--15 6--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 f05b297 D--B 15 20 -- Bipartite Gnm random graph
#> + attr: name (g/c), m (g/n), type (v/l)
#> + edges from f05b297:
#> [1] 7->12 8->12 9->12 3->13 6->13 10->13 2->14 3->14 1->15 10->15
#> [11] 12-> 1 14-> 1 15-> 2 11-> 4 13-> 4 11-> 5 11-> 6 13-> 6 14-> 6 13->10