This function generates two samples of networks according to the stochastic block model (SBM). This is essentially a wrapper around sample_sbm which allows to sample a single network from the SBM.

sample2_sbm(n, nv, p1, b1, p2 = p1, b2 = b1, seed = NULL)

## Arguments

n

Integer scalar giving the sample size.

nv

Integer scalar giving the number of vertices of the generated networks, common to all networks in both samples.

p1

The matrix giving the Bernoulli rates for the 1st sample. This is a KxK 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.

b1

Numeric vector giving the number of vertices in each group for the first sample. The sum of the vector must match the number of vertices.

p2

The matrix giving the Bernoulli rates for the 2nd sample (default: same as 1st sample). This is a KxK 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.

b2

Numeric vector giving the number of vertices in each group for the second sample (default: same as 1st sample). The sum of the vector must match the number of vertices.

seed

The seed for the random number generator (default: NULL).

## Value

A length-2 list containing the two samples stored as nvd objects.

## Examples

n <- 10
p1 <- matrix(
data = c(0.1, 0.4, 0.1, 0.4,
0.4, 0.4, 0.1, 0.4,
0.1, 0.1, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4),
nrow = 4,
ncol = 4,
byrow = TRUE
)
p2 <- matrix(
data = c(0.1, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.1, 0.1,
0.4, 0.4, 0.1, 0.4),
nrow = 4,
ncol = 4,
byrow = TRUE
)
sim <- sample2_sbm(n, 68, p1, c(17, 17, 17, 17), p2, seed = 1234)