# sample_bipartite

From igraph v1.0.0
by Gabor Csardi

##### Bipartite random graphs

Generate bipartite graphs using the Erdos-Renyi model

- Keywords
- 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 $G(n,p)$ 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 isin , edges point from top vertices to bottom vertices. - ...
- 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 probablity $p$, independently of the rest of the edges. In $G(n,m)$, we uniformly choose $m$ edges to realize.

##### Value

- A bipartite igraph graph.

##### See Also

`sample_gnp`

for the unipartite version.

##### Examples

```
## empty graph
sample_bipartite(10, 5, p=0)
## full graph
sample_bipartite(10, 5, p=1)
## random bipartite graph
sample_bipartite(10, 5, p=.1)
## directed bipartite graph, G(n,m)
sample_bipartite(10, 5, type="Gnm", m=20, directed=TRUE, mode="all")
```

*Documentation reproduced from package igraph, version 1.0.0, License: GPL (>= 2)*

### Community examples

Looks like there are no examples yet.