# sample_bipartite

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##### 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 is in, 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.

sample_gnp for the unipartite version.

##### Aliases
• bipartite
• bipartite.random.game
• sample_bipartite
##### 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)

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