# sample_correlated_gnp

From igraph v1.0.0
by Gabor Csardi

##### Generate a new random graph from a given graph by randomly adding/removing edges

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

##### Usage

`sample_correlated_gnp(old.graph, corr, p = old.graph$p, permutation = NULL)`

##### Arguments

- old.graph
- The original graph.
- 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.
- 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

- An unweighted graph of the same size as
`old.graph`

such that the correlation coefficient between the entries of the two adjacency matrices is`corr`

. Note each pair of corresponding matrix entries is a pair of correlated Bernoulli random variables.

##### References

Lyzinski, V., Fishkind, D. E., Priebe, C. E. (2013). Seeded
graph matching for correlated Erdos-Renyi graphs.

##### See Also

##### Examples

```
g <- sample_gnp(1000, .1)
g2 <- sample_correlated_gnp(g, corr = 0.5)
cor(as.vector(g[]), as.vector(g2[]))
g
g2
```

*Documentation reproduced from package igraph, version 1.0.0, License: GPL (>= 2)*

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