Skip to contents

Sample the hierarchical stochastic block model

Source: R/games.R
sample_hierarchical_sbm.Rd

Sampling from a hierarchical stochastic block model of networks.

Usage

sample_hierarchical_sbm(n, m, rho, C, p)

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.

Value

An igraph graph.

Details

The function generates a random graph according to the hierarchical stochastic block model.

hsbm_game(), hsbm_list_game()

See also

Random graph models (games): bipartite_gnm(), erdos.renyi.game(), sample_(), sample_bipartite(), sample_chung_lu(), sample_correlated_gnp(), sample_correlated_gnp_pair(), sample_degseq(), sample_dot_product(), sample_fitness(), sample_fitness_pl(), sample_forestfire(), sample_gnm(), sample_gnp(), sample_grg(), sample_growing(), sample_islands(), sample_k_regular(), sample_last_cit(), sample_pa(), sample_pa_age(), sample_pref(), sample_sbm(), sample_smallworld(), sample_traits_callaway(), sample_tree()

Author

Gabor Csardi csardi.gabor@gmail.com

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
#> ── <igraph> Hierarchical stochastic block model ───────────────────── 8cfd957 ──
#> ℹ undirected
#> ℹ 100 vertices · 455 edges
#> 
#> ── Attributes ──────────────────────────────────────────────────────────────────
#> → graph:  name <chr>, m <dbl>, rho <dbl>, C <dbl>, p <dbl>
#> 
#> ── Edges ───────────────────────────────────────────────────────────────────────
#>   [1] 1 ─ 2     1 ─ 3     2 ─ 3     3 ─ 4     1 ─ 5     2 ─ 5     3 ─ 5    
#>   [8] 1 ─ 6     3 ─ 6     6 ─ 7     4 ─ 8     5 ─ 8     6 ─ 8     5 ─ 9    
#>  [15] 7 ─ 10    8 ─ 10    9 ─ 10    11 ─ 12   11 ─ 13   12 ─ 13   11 ─ 14  
#>  [22] 12 ─ 14   13 ─ 14   11 ─ 15   12 ─ 15   12 ─ 16   13 ─ 16   14 ─ 17  
#>  [29] 16 ─ 17   14 ─ 18   15 ─ 18   16 ─ 18   14 ─ 19   15 ─ 19   16 ─ 19  
#>  [36] 15 ─ 20   16 ─ 20   17 ─ 18   18 ─ 19   17 ─ 20   18 ─ 20   19 ─ 20  
#>  [43] 21 ─ 22   21 ─ 23   22 ─ 23   21 ─ 24   22 ─ 24   21 ─ 25   22 ─ 25  
#>  [50] 21 ─ 26   22 ─ 26   23 ─ 26   25 ─ 27   26 ─ 27   24 ─ 28   25 ─ 28  
#>  [57] 26 ─ 28   25 ─ 29   26 ─ 29   24 ─ 30   25 ─ 30   27 ─ 28   27 ─ 29  
#>  [64] 28 ─ 30   31 ─ 32   31 ─ 33   32 ─ 33   31 ─ 34   32 ─ 34   31 ─ 35  
#> + ... omitted several edges

library("Matrix")
image(g[])