# sample_correlated_gnp_pair

0th

Percentile

##### 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.

Keywords
graphs, random
##### 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

sample_correlated_gnp, sample_gnp.

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

Documentation reproduced from package igraph, version 1.0.1, License: GPL (>= 2)

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