Description Usage Arguments Value References See Also Examples
est.SBA
takes a 2-stage approach for estimating graphons
based on exchangeable random graph models. First, it finds a
Stochastic Blockmodel Approximation (SBA) of the graphon. Then,
it uses clustering information to estimate graphon using a consistent
histogram estimator.
1 | est.SBA(A, delta = 0.5)
|
A |
either
|
delta |
a precision parameter larger than 0. |
a named list containing
a (K\times K) matrix fo 3D histogram.
an (n\times n) corresponding probability matrix.
a length-K list where each element is a vector of nodes/indices for each cluster.
Airoldi2013graphon
\insertRefchan2014graphon
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ## generate a graphon of type No.6 with 3 clusters
W = gmodel.preset(3,id=6)
## create a probability matrix for 100 nodes
graphW = gmodel.block(W,n=100)
P = graphW$P
## draw 17 observations from a given probability matrix
A = gmodel.P(P,rep=17)
## run SBA algorithm with different deltas (0.2,0.5,0.8)
res2 = est.SBA(A,delta=0.2)
res3 = est.SBA(A,delta=0.5)
res4 = est.SBA(A,delta=0.8)
## compare true probability matrix and estimated ones
opar = par(no.readonly=TRUE)
par(mfrow=c(2,2), pty="s")
image(P); title("original P")
image(res2$P); title("SBA with delta=0.2")
image(res3$P); title("SBA with delta=0.5")
image(res4$P); title("SBA with delta=0.8")
par(opar)
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