gmodel.block: Generate binary random graphs based on stochastic blockmodel
In kisungyou/graphon: A Collection of Graphon Estimation Methods

Description Usage Arguments Value See Also Examples

Given a (K\times K) stochastic blockmodel W, gmodel.block generates an (n-by-n) binary random graphs. All K blocks have same number of nodes, or almost identical if n is not a multiple of K. Parameter noloop controls whether generated observations have an edge from a node to itself, called a loop.

1	gmodel.block(W, n, rep = 1, noloop = TRUE)

`W`	a (K\times K) blockmodel matrix.
`n`	the number of nodes for each observation.
`rep`	the number of observations to be generated.
`noloop`	a logical value; TRUE for graphs without self-loops, FALSE otherwise.

a named list containing

G

depending on rep value,

(rep=1): an (n\times n) observation, or
(rep>1): a length-rep list where each element is an observation is an (n\times n) realization from the model.

P

an (n\times n) probability matrix of generating each edge.

gmodel.P

## set inputs
W = matrix(c(0.9,0.2,0.2,0.7),nr=2)
n = 200

## generate 2 observations without self-loops.
out <- gmodel.block(W,n,rep=2,noloop=TRUE)

## visualize generated graphs
opar = par(no.readonly=TRUE)
par(mfrow=c(1,2), pty="s")
image(out$G[[1]]); title("Observation 1")
image(out$G[[2]]); title("Observation 2")
par(opar)