Random Network Model Selection and Parameter Tuning


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Documentation for package ‘randnet’ version 0.2

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randnet-package Statistical modeling of random networks with model selection and parameter tuning
BHMC.estimate Estimates the number of communities under block models by the spectral methods
BlockModel.Gen Generates networks from degree corrected stochastic block model
ConsensusClust clusters nodes by concensus (majority voting) initialized by regularized spectral clustering
DCSBM.estimate Estimates DCSBM model
ECV.block selecting block models by ECV
ECV.nSmooth.lowrank selecting tuning parameter for neighborhood smoothing estimation of graphon model
ECV.Rank estimates optimal low rank model for a network
LRBIC selecting number of communities by asymptotic likelihood ratio
NCV.select selecting block models by NCV
NMI calculates normalized mutual information
nSmooth estimates probabilty matrix by neighborhood smoothing
randnet Statistical modeling of random networks with model selection and parameter tuning
RDPG.Gen generates random networks from random dot product graph model
reg.SP clusters nodes by regularized spectral clustering
reg.SSP detects communities by regularized spherical spectral clustering
SBM.estimate estimates SBM parameters given community labels