sample_sbm | R Documentation |
Sampling from the stochastic block model of networks
sample_sbm(n, pref.matrix, block.sizes, directed = FALSE, loops = FALSE)
sbm(...)
n |
Number of vertices in the graph. |
pref.matrix |
The matrix giving the Bernoulli rates. This is a
|
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 generate a directed graph. |
loops |
Logical scalar, whether self-loops are allowed in the graph. |
... |
Passed to |
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.
An igraph graph.
Gabor Csardi csardi.gabor@gmail.com
Faust, K., & Wasserman, S. (1992a). Blockmodels: Interpretation and evaluation. Social Networks, 14, 5–61.
Random graph models (games)
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()
## 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
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