NSBM.estimate

estimates NSBM parameters given community labels

 NSBM

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.estimate function

<dl> <dt>A</dt>
<dd>adjacency matrix of a directed where Aij = 1 iff i -&gt; j</dd> <dt>K</dt>
<dd>number of communities</dd> <dt>g</dt>
<dd>a vector of community labels</dd> <dt>reg.bound</dt>
<dd>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.</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

estimates nomination SBM parameters given community labels by the method of moments — NSBM.estimate

<dl>

 <dt>A</dt>
<dd>adjacency matrix of a directed where Aij = 1 iff i -&gt; j</dd>

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

 <dt>g</dt>
<dd>a vector of community labels</dd>

 <dt>reg.bound</dt>
<dd>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.</dd>


</dl>

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

NSBM.estimate: estimates nomination SBM parameters given community labels by the method of moments

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