Description
Network Analysis and Community Detection.
Description
Features tools for the network data analysis and community detection. Provides multiple methods for fitting, model selection and goodness-of-fit testing in degree-corrected stochastic blocks models. Most of the computations are fast and scalable for sparse networks, esp. for Poisson versions of the models. Implements the following: Amini, Chen, Bickel and Levina (2013) <doi:10.1214/13-AOS1138> Bickel and Sarkar (2015) <doi:10.1111/rssb.12117> Lei (2016) <doi:10.1214/15-AOS1370> Wang and Bickel (2017) <doi:10.1214/16-AOS1457> Zhang and Amini (2020) <arXiv:2012.15047> Le and Levina (2022) <doi:10.1214/21-EJS1971>.
README.md
nett
nett is an R package for the analysis of network data with a focus on community detection and implements multiple methods for hypothesis testing. It includes model selection and goodness-of-fit tests of SBM/DCSBM to network data, which are useful in network statistical analysis. Some of the implemented functionality are follows:
- Spectral clustering with regularization
- Conditional pseudo-likelihood for community detection (Amini, Chen, Bickel and Levina).
- Spectral goodness-of-test for SBM and DCSBM (inspired by Bickel and Sarkar, and Lei's work).
- Likelihood ratio tests and BIC selection for SBM and DCSBM (inspired by Wang and Bickel's work among others.)
- Likelihood computations for SBM and DCSBM.
- Network Adjusted Chi-square test for SBM and DCSBM (Zhang and Amini).
- Bethe-Hessian Selection for DCSBM (inspired by Le and Levina's work).
- ...
Most of the computations haven been adapted to Poisson models to make them fast and scalable.
Check out the articles for some examples of how to use the package.
Installation
To install, you can use the following command
devtools::install_github("aaamini/nett")
Related repo
See the related repo linfanz/nac-test, for some experiments comparing goodness-of-fit and model selection approaches.