| sample2_sbm | R Documentation | 
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)
| 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:  | 
A length-2 list containing the two samples stored as
nvd objects.
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)
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