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randnet

Random Network Model Selection and Parameter Tuning

Model selection and parameter tuning procedures for a class of random network models. The model selection can be done by a general cross-validation framework called ECV from Li et. al. (2016) <arXiv:1612.04717> . Several other model-based and task-specific methods are also included, such as NCV from Chen and Lei (2016) <arXiv:1411.1715>, likelihood ratio method from Wang and Bickel (2015) <arXiv:1502.02069>, spectral methods from Le and Levina (2015) <arXiv:1507.00827>. Many network analysis methods are also implemented, such as the regularized spectral clustering (Amini et. al. 2013 <doi:10.1214/13-AOS1138>) and its degree corrected version and graphon neighborhood smoothing (Zhang et. al. 2015 <arXiv:1509.08588>).

Functions (16)

BHMC.estimate

Estimates the number of communities under block models by the spectral methods

BlockModel.Gen

Generates networks from degree corrected stochastic block model

ConcensusClust

clusters nodes by concensus (majority voting) initialized by regularized spectral clustering

DCSBM.estimate

Estimates DCSBM model

ECV.Rank

estimates optimal low rank model for a network

ECV.block

selecting block models by ECV

ECV.nSmooth.lowrank

selecting tuning parameter for neighborhood smoothing estimation of graphon model

LRBIC

selecting number of communities by asymptotic likelihood ratio

NCV.select

selecting block models by NCV

NMI

calculates normalized mutual information

RDPG.Gen

generates random networks from random dot product graph model

SBM.estimate

estimates SBM parameters given community labels

nSmooth

estimates probabilty matrix by neighborhood smoothing

randnet-package

Statistical modeling of random networks with model selection and parameter tuning

reg.SP

clusters nodes by regularized spectral clustering

reg.SSP

detects communities by regularized spherical spectral clustering

randnet

Random Network Model Selection and Parameter Tuning

v0.2
GPL (>= 2)
Authors
Tianxi Li, Elizaveta Levina, Ji Zhu
Initial release
2019-02-10

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