Sampling from the stochastic block model of networks
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 create 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.
References
Faust, K., & Wasserman, S. (1992a). Blockmodels: Interpretation and evaluation. Social Networks, 14, 5–61.
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_hierarchical_sbm(),
sample_islands(),
sample_k_regular(),
sample_last_cit(),
sample_pa(),
sample_pa_age(),
sample_pref(),
sample_smallworld(),
sample_traits_callaway(),
sample_tree()
Author
Gabor Csardi csardi.gabor@gmail.com
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
#> IGRAPH 69348ed U--- 1000 17060 -- Stochastic block model
#> + attr: name (g/c), loops (g/l)
#> + edges from 69348ed:
#> [1] 3-- 4 1-- 6 4-- 9 6--10 2--11 2--12 4--14 9--15 12--15 13--15
#> [11] 14--16 10--17 9--18 15--19 1--20 2--20 11--20 19--20 1--21 4--21
#> [21] 16--21 4--22 7--22 15--22 21--22 2--23 6--23 18--23 20--23 18--24
#> [31] 10--25 11--25 16--25 7--26 15--26 19--26 7--27 14--27 17--27 20--27
#> [41] 21--28 11--29 13--29 26--29 2--30 10--30 18--30 5--31 19--31 22--31
#> [51] 28--31 29--31 25--32 4--33 19--33 27--33 28--33 1--34 8--34 11--34
#> [61] 8--35 8--36 11--36 2--37 7--37 29--37 35--37 1--38 24--38 29--38
#> [71] 33--38 35--38 37--38 8--39 9--39 31--39 14--40 38--40 27--41 33--41
#> + ... omitted several edges