rgnm {sna} | R Documentation |
rgnm
generates random draws from a density-conditioned uniform random graph distribution.
rgnm(n, nv, m, mode = "digraph", diag = FALSE)
n |
the number of graphs to generate. |
nv |
the size of the vertex set (|V(G)|) for the random graphs. |
m |
the number of edges on which to condition. |
mode |
"digraph" for directed graphs, or "graph" for undirected graphs. |
diag |
boolean; should loops be allowed? |
rgnm
returns draws from the density-conditioned uniform random graph first popularized by the famous work of Erd"{o}s and R'{e}nyi (the G(N,M) process). In particular, the pmf of a G(N,M) process is given by
p(G=g|N,M) = 1/Choose(E_m,M)
where E_m is the maximum number of edges in the graph. (E_m is equal to nv*(nv-diag)/(1+(mode=="graph"))
.)
The G(N,M) process is one of several process which are used as baseline models of social structure. Other well-known baseline models include the Bernoulli graph (the G(N,p) model of Erd"{o}s and R'{e}nyi) and the U|MAN model of dyadic independence. These are implemented within sna
as rgraph
and rgnm
, respectively.
A matrix or array containing the drawn adjacency matrices
Carter T. Butts buttsc@uci.edu
Erd"{o}s, P. and R'{e}nyi, A. (1960). ``On the Evolution of Random Graphs.'' Public Mathematical Institute of Hungary Academy of Sciences, 5:17-61.
#Draw 5 random graphs of order 10 all(gden(rgnm(5,10,9,mode="graph"))==0.2) #Density 0.2 all(gden(rgnm(5,10,9))==0.1) #Density 0.1 #Plot a random graph gplot(rgnm(1,10,20))