# R igraph manual pages

Use this if you are using igraph from R

 sample_sbm {igraph} R Documentation

## Sample stochastic block model

### Description

Sampling from the stochastic block model of networks

### Usage

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

sbm(...)


### 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. The order of the vertices in the generated graph corresponds to the block.sizes argument.

An igraph graph.

### Author(s)

Gabor Csardi csardi.gabor@gmail.com

### References

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

sample_gnp, sample_gnm

### 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


[Package igraph version 1.3.2 Index]