sample_correlated_gnp_pair | R Documentation |
G(n,p)
random graphsSample 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)
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 |
Please see the reference given below.
A list of two igraph objects, named graph1
and
graph2
, which are two graphs whose adjacency matrix entries are
correlated with corr
.
Lyzinski, V., Fishkind, D. E., Priebe, C. E. (2013). Seeded graph matching for correlated Erdős-Rényi graphs. https://arxiv.org/abs/1304.7844
Random graph models (games)
erdos.renyi.game()
,
sample_()
,
sample_bipartite()
,
sample_chung_lu()
,
sample_correlated_gnp()
,
sample_degseq()
,
sample_dot_product()
,
sample_fitness()
,
sample_fitness_pl()
,
sample_forestfire()
,
sample_gnm()
,
sample_gnp()
,
sample_grg()
,
sample_growing()
,
sample_hierarchical_sbm()
,
sample_islands()
,
sample_k_regular()
,
sample_last_cit()
,
sample_pa()
,
sample_pa_age()
,
sample_pref()
,
sample_sbm()
,
sample_smallworld()
,
sample_traits_callaway()
,
sample_tree()
gg <- sample_correlated_gnp_pair(
n = 10, corr = .8, p = .5,
directed = FALSE
)
gg
cor(as.vector(gg[[1]][]), as.vector(gg[[2]][]))
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