# sample_hierarchical_sbm

##### Sample the hierarchical stochastic block model

Sampling from a hierarchical stochastic block model of networks.

##### Usage

`sample_hierarchical_sbm(n, m, rho, C, p)`

##### Arguments

- n
Integer scalar, the number of vertices.

- m
Integer scalar, the number of vertices per block.

`n / m`

must be integer. Alternatively, an integer vector of block sizes, if not all the blocks have equal sizes.- rho
Numeric vector, the fraction of vertices per cluster, within a block. Must sum up to 1, and

`rho * m`

must be integer for all elements of rho. Alternatively a list of rho vectors, one for each block, if they are not the same for all blocks.- C
A square, symmetric numeric matrix, the Bernoulli rates for the clusters within a block. Its size must mach the size of the

`rho`

vector. Alternatively, a list of square matrices, if the Bernoulli rates differ in different blocks.- p
Numeric scalar, the Bernoulli rate of connections between vertices in different blocks.

- …
Passed to

`sample_hierarchical_sbm`

.

##### Details

The function generates a random graph according to the hierarchical stochastic block model.

##### Value

An igraph graph.

##### See Also

##### Examples

```
# NOT RUN {
## Ten blocks with three clusters each
C <- matrix(c(1 , 3/4, 0,
3/4, 0, 3/4,
0 , 3/4, 3/4), nrow=3)
g <- sample_hierarchical_sbm(100, 10, rho=c(3, 3, 4)/10, C=C, p=1/20)
g
if (require(Matrix)) { image(g[]) }
# }
```

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