NSBM.estimate: estimates nomination SBM parameters given community labels by...

In randnet: Random Network Model Estimation, Selection and Parameter Tuning

estimates nomination SBM parameters given community labels by the method of moments

Description

estimates NSBM parameters given community labels

Usage

NSBM.estimate(A,K,g,reg.bound=-Inf)

Arguments

A	adjacency matrix of a directed where Aij = 1 iff i -> j
K	number of communities
g	a vector of community labels
reg.bound	the regularity lower bound of lambda value. By default, -Inf. That means, no constraints. When the network is sparse, using certain constaints may improve stability.

Details

The method of moments is used for estimating the edge nomination SBM, so the strategy can be used for both unweighted and weighted networks. The details can be found in Li et. al. (2020).

Value

a list of

B	estimated block connection probability matrix
lambda	estimated lambda values for nomination intensity
theta	estimated theta values for nomination preference
P.tilde	estimated composiste probability matrix after nomination
g	community labels

Author(s)

Tianxi Li, Elizaveta Levina, Ji Zhu
Maintainer: Tianxi Li [e-mail]

References

T. Li, E. Levina, and J. Zhu. Community models for networks observed through edge nominations. arXiv preprint arXiv:2008.03652 (2020).

See Also

SBM.estimate

Examples

dt <- NSBM.Gen(n=200,K=2,beta=0.2,avg.d=10)


A <- dt$A


sc <- RightSC(A,K=3)
est <- NSBM.estimate(A,K=3,g=sc$cluster)

randnet documentation built on May 31, 2023, 6:44 p.m.