# sample_correlated_gnp_pair

##### Sample a pair of correlated G(n,p) random graphs

Sample a new graph by perturbing the adjacency matrix of a given graph and shuffling its vertices.

##### Usage

`sample_correlated_gnp_pair(n, corr, p, directed = FALSE, permutation = NULL)`

##### Arguments

- n
Numeric scalar, the number of vertices for the sampled graphs.

- corr
A scalar in the unit interval, the target Pearson correlation between the adjacency matrices of the original the generated graph (the adjacency matrix being used as a vector).

- p
A numeric scalar, the probability of an edge between two vertices, it must in the open (0,1) interval.

- directed
Logical scalar, whether to generate directed graphs.

- permutation
A numeric vector, a permutation vector that is applied on the vertices of the first graph, to get the second graph. If

`NULL`

, the vertices are not permuted.

##### Details

Please see the reference given below.

##### Value

A list of two igraph objects, named `graph1`

and
`graph2`

, which are two graphs whose adjacency matrix entries are
correlated with `corr`

.

##### References

Lyzinski, V., Fishkind, D. E., Priebe, C. E. (2013). Seeded graph matching for correlated Erdos-Renyi graphs. http://arxiv.org/abs/1304.7844

##### See Also

##### Examples

```
# NOT RUN {
gg <- sample_correlated_gnp_pair(n = 10, corr = .8, p = .5,
directed = FALSE)
gg
cor(as.vector(gg[[1]][]), as.vector(gg[[2]][]))
# }
```

*Documentation reproduced from package igraph, version 1.2.2, License: GPL (>= 2)*