sample_correlated_gnp: Generate a new random graph from a given graph by randomly
adding/removing edges
Description
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 = edge_density(old.graph),
permutation = NULL
)
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.
Arguments
old.graph
The original graph.
corr
A scalar in the unit interval, the target Pearson
correlation between the adjacency matrices of the original and 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. The default is the empirical
edge density of the graph. If you are resampling an Erdos-Renyi graph and
you know the original edge probability of the Erdos-Renyi model, you should
supply that explicitly.
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.
References
Lyzinski, V., Fishkind, D. E., Priebe, C. E. (2013). Seeded
graph matching for correlated Erdos-Renyi graphs.
https://arxiv.org/abs/1304.7844