# sample_sbm

##### Sample stochastic block model

Sampling from the stochastic block model of networks

- Keywords
- graphs

##### Usage

`sample_sbm(n, pref.matrix, block.sizes, directed = FALSE, loops = FALSE)`

##### Arguments

- n
Number of vertices in the graph.

- pref.matrix
The matrix giving the Bernoulli rates. This is a \(K\times K\) matrix, where \(K\) is the number of groups. The probability of creating an edge between vertices from groups \(i\) and \(j\) is given by element \((i,j)\). For undirected graphs, this matrix must be symmetric.

- block.sizes
Numeric vector giving the number of vertices in each group. The sum of the vector must match the number of vertices.

- directed
Logical scalar, whether to generate a directed graph.

- loops
Logical scalar, whether self-loops are allowed in the graph.

- …
Passed to

`sample_sbm`

.

##### Details

This function samples graphs from a stochastic block model by (doing the
equivalent of) Bernoulli trials for each potential edge with the
probabilities given by the Bernoulli rate matrix, `pref.matrix`

.

##### Value

An igraph graph.

##### References

Faust, K., & Wasserman, S. (1992a). Blockmodels: Interpretation
and evaluation. *Social Networks*, 14, 5--61.

##### See Also

##### Examples

```
# NOT RUN {
## Two groups with not only few connection between groups
pm <- cbind( c(.1, .001), c(.001, .05) )
g <- sample_sbm(1000, pref.matrix=pm, block.sizes=c(300,700))
g
# }
```

*Documentation reproduced from package igraph, version 1.0.1, License: GPL (>= 2)*