NSBM.Gen: Generates networks from nomination stochastic block model

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

Generates networks from nomination stochastic block model

Description

Generates networks from nomination stochastic block model for community structure in edge nomination procedures, proposed in Li et. al. (2020)

Usage

NSBM.Gen( n, K, avg.d,beta,theta.low=0.1,
    theta.p=0.2,lambda.scale=0.2,lambda.exp=FALSE)

Arguments

n	size of network
K	number of communities
avg.d	expected average degree of the resuling network (after edge nomination)
beta	the out-in ratio of the original SBM
theta.low	the lower value of theta's. The theta's are generated as two-point mass at theta.low and 1.
theta.p	proportion of lower value of theta's
lambda.scale	standard deviation of the lambda (before the exponential, see lambda.exp)
lambda.exp	If TRUE, lambda is generated as exponential of uniformation random randomes. Otherwise, they are normally distributed.

Value

A list of

A	the generated network adjacency matrix
g	community membership
P	probability matrix of the orignal SBM network
P.tilde	probability matrix of the observed network after nomination
B	B parameter
lambda	lambda parameter
theta	theta parameter

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).

Examples

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

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