est.nbdsmooth takes the expectation of the adjacency matrix
in that it directly aims at estimating network edge probabilities without
imposing structural assumptions as of usual graphon estimation requires,
such as piecewise lipschitz condition. Note that this method is
for symmetric adjacency matrix only, i.e., undirected networks.
a named list containing
a quantile threshold value.
a matrix of estimated edge probabilities.
Zhang, Y., Levina, E., and Zhu, J. (2015) Estimating neighborhood edge probabilities by neighborhood smoothing. Arxiv:1509.08588
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## generate a graphon of type No.4 with 3 clusters W = gmodel.preset(3,id=4) ## create a probability matrix for 100 nodes graphW = gmodel.block(W,n=100) P = graphW$P ## draw 5 observations from a given probability matrix A = gmodel.P(P,rep=5,symmetric.out=TRUE) ## run nbdsmooth algorithm res2 = est.nbdsmooth(A) ## compare true probability matrix and estimated ones par(mfrow=c(1,2)) image(P); title("original P") image(res2$P); title("nbdsmooth estimated P")
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