# sample_hierarchical_sbm

From igraph v1.0.0
by Gabor Csardi

##### 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

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

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