USVT

estimates the network probability matrix by the universal singular value thresholding of Chatterjee (2015), with the improvement mentioned in Zhang et. al. (2017).

 singular value thresholding

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 

USVT function

<dl> <dt>A</dt>
<dd>adjacency matrix</dd>
</dl>

Arguments

Tianxi Li and Can M. Le 
 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 the network probability matrix by the improved universal singular value thresholding — USVT

<dl>

 <dt>A</dt>
<dd>adjacency matrix</dd>




</dl>

Tianxi Li and Can M. Le 
 Maintainer: Tianxi Li <a href='mailto:tianxili@virginia.edu'>tianxili@virginia.edu</a>

USVT: estimates the network probability matrix by the improved universal singular value thresholding

Description

Usage

Value

Arguments

Author

Details

References

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Examples