# sample_correlated_gnp_pair

From igraph v1.0.0
by Gabor Csardi

##### 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.

##### See Also

##### Examples

```
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 1.0.0, License: GPL (>= 2)*

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