`rgraph`

generates random draws from a Bernoulli graph distribution, with various parameters for controlling the nature of the data so generated.

```
rgraph(n, m=1, tprob=0.5, mode="digraph", diag=FALSE, replace=FALSE,
tielist=NULL, return.as.edgelist=FALSE)
```

n

The size of the vertex set (|V(G)|) for the random graphs

m

The number of graphs to generate

tprob

Information regarding tie (edge) probabilities; see below

mode

``digraph'' for directed data, ``graph'' for undirected data

diag

Should the diagonal entries (loops) be set to zero?

replace

Sample with or without replacement from a tie list (ignored if `tielist==NULL`

tielist

A vector of edge values, from which the new graphs should be bootstrapped

return.as.edgelist

logical; should the resulting graphs be returned in edgelist form?

A graph stack

`rgraph`

is a reasonably versatile routine for generating random network data. The graphs so generated are either Bernoulli graphs (graphs in which each edge is a Bernoulli trial, independent conditional on the Bernoulli parameters), or are bootstrapped from a user-provided edge distribution (very handy for CUG tests). In the latter case, edge data should be provided using the `tielist`

argument; the exact form taken by the data is irrelevant, so long as it can be coerced to a vector. In the former case, Bernoulli graph probabilities are set by the `tprob`

argument as follows:

If

`tprob`

contains a single number, this number is used as the probability of all edges.If

`tprob`

contains a vector, each entry is assumed to correspond to a separate graph (in order). Thus, each entry is used as the probability of all edges within its corresponding graph.If

`tprob`

contains a matrix, then each entry is assumed to correspond to a separate edge. Thus, each entry is used as the probability of its associated edge in each graph which is generated.Finally, if

`tprob`

contains a three-dimensional array, then each entry is assumed to correspond to a particular edge in a particular graph, and is used as the associated probability parameter.

Finally, note that `rgraph`

will symmetrize all generated networks if `mode`

is set to ``graph'' by copying down the upper triangle. The lower half of `tprob`

, where applicable, must still be specified, however.

Erdos, P. and Renyi, A. (1960). “On the Evolution of Random Graphs.” *Public Mathematical Institute of Hungary Academy of Sciences,* 5:17-61.

Wasserman, S., and Faust, K. (1994). *Social Network Analysis: Methods and Applications*. Cambridge: Cambridge University Press.

```
# NOT RUN {
#Generate three graphs with different densities
g<-rgraph(10,3,tprob=c(0.1,0.9,0.5))
#Generate from a matrix of Bernoulli parameters
g.p<-matrix(runif(25,0,1),nrow=5)
g<-rgraph(5,2,tprob=g.p)
# }
```

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