test-hsbm.R
In igraph: Network Analysis and Visualization

test_that("HSBM works", {
  set.seed(42)

  C <- matrix(c(
    1, 1 / 2, 0,
    1 / 2, 0, 1 / 2,
    0, 1 / 2, 1 / 2
  ), nrow = 3)

  g <- sample_hierarchical_sbm(100, 10, rho = c(3, 3, 4) / 10, C = C, p = 0)
  expect_that(ecount(g), equals(172))
  expect_that(vcount(g), equals(100))
  expect_false(is.directed(g))

  set.seed(42)

  g2 <- sample_hierarchical_sbm(100, 10, rho = c(3, 3, 4) / 10, C = C, p = 1)
  expect_that(ecount(g2), equals(ecount(g) + 10 * 9 * (90 + 10) / 2))
  expect_that(vcount(g2), equals(100))
  expect_true(is.simple(g2))

  set.seed(42)

  g3 <- sample_hierarchical_sbm(100, 10, rho = c(3, 3, 4) / 10, C = C, p = 1e-15)
  expect_that(ecount(g3), equals(ecount(g)))
  expect_that(vcount(g3), equals(100))
  expect_true(is.simple(g3))

  set.seed(42)

  g4 <- sample_hierarchical_sbm(100, 10, rho = c(3, 3, 4) / 10, C = C, p = 1 - 1e-15)
  expect_that(ecount(g4), equals(ecount(g2)))
  expect_that(vcount(g4), equals(100))
  expect_true(is.simple(g4))
})

test_that("HSBM with 1 cluster per block works", {
  res <- Matrix::Matrix(0, nrow = 10, ncol = 10, doDiag = FALSE)
  res[6:10, 1:5] <- res[1:5, 6:10] <- 1
  g <- sample_hierarchical_sbm(10, 5, rho = 1, C = matrix(0), p = 1)
  expect_that(g[], equals(res))
})

test_that("HSBM with list arguments works", {
  b <- 5
  C <- matrix(c(
    1, 1 / 2, 0,
    1 / 2, 0, 1 / 2,
    0, 1 / 2, 1 / 2
  ), nrow = 3)
  m <- 10
  rho <- c(3, 3, 4) / 10

  set.seed(42)
  g <- sample_hierarchical_sbm(b * m, m, rho = rho, C = C, p = 0)

  set.seed(42)
  g2 <- sample_hierarchical_sbm(b * m, rep(m, b), rho = rho, C = C, p = 0)
  expect_that(g[], equals(g2[]))

  set.seed(42)
  g3 <- sample_hierarchical_sbm(b * m, m, rho = replicate(b, rho, simplify = FALSE), C = C, p = 0)
  expect_that(g[], equals(g3[]))

  set.seed(42)
  g4 <- sample_hierarchical_sbm(b * m, m, rho = rho, C = replicate(b, C, simplify = FALSE), p = 0)
  expect_that(g[], equals(g4[]))

  expect_that(
    sample_hierarchical_sbm(b * m, rep(m, b), rho = list(rho, rho), C = C, p = 0),
    throws_error("Lengths of `m', `rho' and `C' must match")
  )

  ###

  n <- function(x) x / sum(x)

  rho1 <- n(c(1, 2))
  C1 <- matrix(0, nrow = 2, ncol = 2)
  rho2 <- n(c(3, 3, 4))
  C2 <- matrix(0, nrow = 3, ncol = 3)
  rho3 <- 1
  C3 <- matrix(0)
  rho4 <- n(c(2, 1))
  C4 <- matrix(0, nrow = 2, ncol = 2)

  gg1 <- sample_hierarchical_sbm(21,
    m = c(3, 10, 5, 3), rho = list(rho1, rho2, rho3, rho4),
    C = list(C1, C2, C3, C4), p = 1
  )
  expect_true(is.simple(gg1))

  set.seed(42)
  gg11 <- sample_hierarchical_sbm(21,
    m = c(3, 10, 5, 3), rho = list(rho1, rho2, rho3, rho4),
    C = list(C1, C2, C3, C4), p = 1 - 1e-10
  )
  expect_that(gg1[], equals(gg11[]))

  rho1 <- n(c(1, 2))
  C1 <- matrix(1, nrow = 2, ncol = 2)
  rho2 <- n(c(3, 3, 4))
  C2 <- matrix(1, nrow = 3, ncol = 3)
  rho3 <- 1
  C3 <- matrix(1)
  rho4 <- n(c(2, 1))
  C4 <- matrix(1, nrow = 2, ncol = 2)
  gg2 <- sample_hierarchical_sbm(21,
    m = c(3, 10, 5, 3), rho = list(rho1, rho2, rho3, rho4),
    C = list(C1, C2, C3, C4), p = 0
  )
  expect_true(is.simple(gg2))

  gg22 <- sample_hierarchical_sbm(21,
    m = c(3, 10, 5, 3), rho = list(rho1, rho2, rho3, rho4),
    C = list(C1, C2, C3, C4), p = 1
  )
  expect_that(gg1[] + gg2[], equals(gg22[]))
})