# sample_correlated_gnp

0th

Percentile

##### 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. http://arxiv.org/abs/1304.7844

sample_correlated_gnp_pair, sample_gnp

##### Aliases
• sample_correlated_gnp
##### Examples
# NOT RUN {
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 1.2.3, License: GPL (>= 2)

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