nSmooth {randnet} | R Documentation |
estimates probabilty matrix by neighborhood smoothing
Description
estimates probabilty matrix by neighborhood smoothing of Zhang et. al. (2017)
Usage
nSmooth(A, h = NULL)
Arguments
A |
adjacency matrix |
h |
quantile value used for smoothing. Recommended to be in the scale of sqrt(log(n)/n) where n is the size of the network. The default value is sqrt(log(n)/n) from the paper. |
Details
The method assumes a graphon model where the underlying graphon function is piecewise Lipchitz. However, it may be slow for moderately large networks, though it is one of the fastest methods for graphon models.
Value
the probability matrix
Author(s)
Tianxi Li, Elizaveta Levina, Ji Zhu
Maintainer: Tianxi Li <tianxili@virginia.edu>
References
Zhang, Y.; Levina, E. & Zhu, J. Estimating network edge probabilities by neighbourhood smoothing Biometrika, Oxford University Press, 2017, 104, 771-783
Examples
N <- 100
U = matrix(1:N,nrow=1) / (N+1)
V = matrix(1:N,nrow=1) / (N+1)
W = (t(U))^2
W = W/3*cos(1/(W + 1e-7)) + 0.15
upper.index <- which(upper.tri(W))
A <- matrix(0,N,N)
rand.ind <- runif(length(upper.index))
edge.index <- upper.index[rand.ind < W[upper.index]]
A[edge.index] <- 1
A <- A + t(A)
diag(A) <- 0
What <- nSmooth(A)
[Package randnet version 0.7 Index]