# sample_sbm

From igraph v1.0.0
by Gabor Csardi

##### 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 s
- 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

```
## 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 1.0.0, License: GPL (>= 2)*

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