R/games.R
sample_correlated_gnp.RdSample a new graph by perturbing the adjacency matrix of a given graph and shuffling its vertices.
sample_correlated_gnp(
old.graph,
corr,
p = edge_density(old.graph),
permutation = NULL
)The original graph.
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).
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 Erdős-Rényi graph and you know the original edge probability of the Erdős-Rényi model, you should supply that explicitly.
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.
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.
Please see the reference given below.
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_pair(),
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()
g <- sample_gnp(1000, .1)
g2 <- sample_correlated_gnp(g, corr = 0.5)
cor(as.vector(g[]), as.vector(g2[]))
#> [1] 0.499866
g
#> IGRAPH cab2fe2 U--- 1000 49982 -- Erdos-Renyi (gnp) graph
#> + attr: name (g/c), type (g/c), loops (g/l), p (g/n)
#> + edges from cab2fe2:
#> [1] 1-- 5 7--10 8--10 8--11 11--14 13--15 10--16 11--16 7--19 10--19
#> [11] 16--19 4--20 5--22 6--23 16--23 22--23 2--24 18--24 9--25 16--25
#> [21] 21--25 24--25 4--26 24--26 13--27 17--27 23--27 14--28 18--28 21--28
#> [31] 1--29 7--30 19--30 3--31 21--31 1--32 18--32 21--32 12--33 13--33
#> [41] 28--33 9--34 20--34 25--34 7--35 8--35 26--35 28--35 29--35 33--35
#> [51] 1--36 13--36 28--36 29--36 28--37 34--37 15--38 31--38 37--38 9--39
#> [61] 13--39 22--39 32--39 1--40 8--40 2--41 8--41 12--41 15--41 19--41
#> [71] 13--42 26--42 33--42 19--43 25--43 33--43 7--44 15--44 16--44 21--44
#> + ... omitted several edges
g2
#> IGRAPH 8a093ce U--- 1000 49602 -- Correlated random graph
#> + attr: name (g/c), corr (g/n), p (g/n)
#> + edges from 8a093ce:
#> [1] 1-- 2 8--10 5--11 8--11 5--12 10--12 8--15 13--15 2--16 10--16
#> [11] 11--16 17--19 5--20 5--22 19--22 6--23 16--23 19--23 22--23 2--24
#> [21] 18--24 19--24 9--25 12--25 24--25 23--26 24--26 23--27 4--28 14--28
#> [31] 18--28 21--28 1--29 11--29 3--30 7--30 19--30 9--31 21--31 4--32
#> [41] 18--32 21--32 6--33 12--33 13--33 16--33 4--35 7--35 8--35 10--35
#> [51] 20--35 26--35 29--35 33--35 8--36 18--36 22--36 28--36 29--36 2--37
#> [61] 25--37 28--37 31--37 7--38 15--38 26--38 31--38 37--38 9--39 13--39
#> [71] 22--39 29--39 1--40 17--40 22--40 29--40 2--41 9--42 13--42 33--42
#> + ... omitted several edges