NSBM.Gen

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

 BlockModel.Gen 

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>). It also includes the consensus clustering of Gao et. al. (2014) <arXiv:1410.5837>, the method of moments estimation of nomination SBM of Li et. al. (2020) <arxiv:2008.03652>, and the network mixing method of Li and Le (2021) <arxiv:2106.02803>. It also includes the informative core-periphery data processing method of Miao and Li (2021) <arXiv:2101.06388>. The work to build and improve this package is partially supported by the NSF grants DMS-2015298 and DMS-2015134.

Tianxi Li

randnet

Random Network Model Estimation, Selection and Parameter Tuning

Tianxi Li 

Elizeveta Levina 

Ji Zhu 

Can M. Le 

NSBM.Gen function

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

Arguments

Tianxi Li, Elizaveta Levina, Ji Zhu 
 Maintainer: Tianxi Li <a href="/link/tianxili%40virginia.edu?package=randnet&version=0.7" data-mini-rdoc="randnet::tianxili@virginia.edu">tianxili@virginia.edu</a>

Author

Generates networks from nomination stochastic block model — NSBM.Gen

<dl>

 <dt>n</dt>
<dd>size of network</dd>

 <dt>K</dt>
<dd>number of communities</dd>

 <dt>avg.d</dt>
<dd>expected average degree of the resuling network (after edge nomination)</dd>

 <dt>beta</dt>
<dd>the out-in ratio of the original SBM</dd>

 <dt>theta.low</dt>
<dd>the lower value of theta's. The theta's are generated as two-point mass at theta.low and 1.</dd>

 <dt>theta.p</dt>
<dd>proportion of lower value of theta's</dd>

 <dt>lambda.scale</dt>
<dd>standard deviation of the lambda (before the exponential, see lambda.exp)</dd>

 <dt>lambda.exp</dt>
<dd>If TRUE, lambda is generated as exponential of uniformation random randomes. Otherwise, they are normally distributed.</dd>

</dl>

Tianxi Li, Elizaveta Levina, Ji Zhu 
 Maintainer: Tianxi Li <a href='mailto:tianxili@virginia.edu'>tianxili@virginia.edu</a>

NSBM.Gen: Generates networks from nomination stochastic block model

Description

Usage

Value

Arguments

Author

References

Examples