This function can return a sparse or dense bipartite adjacency matrix of a bipartite network. The bipartite adjacency matrix is an \(n\) times \(m\) matrix, \(n\) and \(m\) are the number of vertices of the two kinds.

## Arguments

- graph
The input graph. The direction of the edges is ignored in directed graphs.

- types
An optional vertex type vector to use instead of the

`type`

vertex attribute. You must supply this argument if the graph has no`type`

vertex attribute.- attr
Either

`NULL`

or a character string giving an edge attribute name. If`NULL`

, then a traditional bipartite adjacency matrix is returned. If not`NULL`

then the values of the given edge attribute are included in the bipartite adjacency matrix. If the graph has multiple edges, the edge attribute of an arbitrarily chosen edge (for the multiple edges) is included.- names
Logical scalar, if

`TRUE`

and the vertices in the graph are named (i.e. the graph has a vertex attribute called`name`

), then vertex names will be added to the result as row and column names. Otherwise the ids of the vertices are used as row and column names.- sparse
Logical scalar, if it is

`TRUE`

then a sparse matrix is created, you will need the`Matrix`

package for this.

## Details

Bipartite graphs have a `type`

vertex attribute in igraph, this is
boolean and `FALSE`

for the vertices of the first kind and `TRUE`

for vertices of the second kind.

Some authors refer to the bipartite adjacency matrix as the "bipartite incidence matrix". igraph 1.6.0 and later does not use this naming to avoid confusion with the edge-vertex incidence matrix.

## See also

`graph_from_biadjacency_matrix()`

for the opposite operation.

Other conversion:
`as.matrix.igraph()`

,
`as_adj_list()`

,
`as_adjacency_matrix()`

,
`as_data_frame()`

,
`as_directed()`

,
`as_edgelist()`

,
`as_graphnel()`

,
`as_long_data_frame()`

,
`graph_from_adj_list()`

,
`graph_from_graphnel()`

## Author

Gabor Csardi csardi.gabor@gmail.com

## Examples

```
g <- make_bipartite_graph(c(0, 1, 0, 1, 0, 0), c(1, 2, 2, 3, 3, 4))
as_biadjacency_matrix(g)
#> 2 4
#> 1 1 0
#> 3 1 1
#> 5 0 0
#> 6 0 0
```