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.
sample_correlated_gnp_pair(n, corr, p, directed = FALSE, permutation = NULL)
- Numeric scalar, the number of vertices for the sampled graphs.
- 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).
- A numeric scalar, the probability of an edge between two vertices, it must in the open (0,1) interval.
- Logical scalar, whether to generate directed graphs.
- 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.
Please see the reference given below.
- A list of two igraph objects, named
graph2, which are two graphs whose adjacency matrix entries are correlated with
Lyzinski, V., Fishkind, D. E., Priebe, C. E. (2013). Seeded
graph matching for correlated Erdos-Renyi graphs.
gg <- sample_correlated_gnp_pair(n = 10, corr = .8, p = .5, directed = FALSE) gg cor(as.vector(gg[]), as.vector(gg[]))