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

## Description

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.

## Usage

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

## Arguments

 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.

## Value

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
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 ## Not run: ## 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 par(mfrow=c(1,2)) image(out$G[[1]]); title("Observation 1") image(out$G[[2]]); title("Observation 2") ## End(Not run)