sample_correlated_sbm_pair | R Documentation |
Sample a pair of random graphs from stochastic block model with
correlation between two graphs being corr
and edge probability being
p
.
sample_correlated_sbm_pair(
n,
pref.matrix,
block.sizes,
corr,
core.block.sizes = NULL,
permutation = 1:n,
...
)
n |
An integer. Number of vertices in the graph. |
pref.matrix |
The matrix giving the Bernoulli rates. This is a
|
block.sizes |
A numeric vector. Give the number of vertices in each group. The sum of the vector must match the number of vertices. |
corr |
A number. The target Pearson correlation between the adjacency matrices of the generated graphs. It must be in open (0,1) interval. |
core.block.sizes |
A numeric vector. Give the number of core vertices in
each group. Entries should be smaller than |
permutation |
A numeric vector, permute second graph. |
... |
Passed to |
Returns a list of two igraph object, named graph1
and
graph2
. If sample two graphs with junk vertices, in each
corresponding block the first core.block.sizes
vertices are core
vertices and the rest are junk vertices.
P. Holland and K. Laskey and S. Leinhardt (1983), Stochastic Blockmodels: First Steps. Social Networks, pages 109-137.
F. Fang and D. Sussman and V. Lyzinski (2018), Tractable Graph Matching via Soft Seeding. https://arxiv.org/abs/1807.09299.
sample_correlated_gnp_pair
,
sample_correlated_rdpg_pair
pm <- cbind( c(.1, .001), c(.001, .05) )
sample_correlated_sbm_pair(n=1000, pref.matrix=pm, block.sizes=c(300,700), corr=0.5)
sample_correlated_sbm_pair(n=1000, pref.matrix=pm, block.sizes=c(300,700), corr=0.5,
core.block.sizes=c(200,500))
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