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rSpectral  

Spectral Modularity Clustering
View on CRAN: Click here


Download and install rSpectral package within the R console
Install from CRAN:
install.packages("rSpectral")

Install from Github:
library("remotes")
install_github("cran/rSpectral")

Install by package version:
library("remotes")
install_version("rSpectral", "1.0.0.10")



Attach the package and use:
library("rSpectral")
Maintained by
Anatoly Sorokin
[Scholar Profile | Author Map]
First Published: 2022-08-19
Latest Update: 2023-01-18
Description:
Implements the network clustering algorithm described in Newman (2006) . The complete iterative algorithm comprises of two steps. In the first step, the network is expressed in terms of its leading eigenvalue and eigenvector and recursively partition into two communities. Partitioning occurs if the maximum positive eigenvalue is greater than the tolerance (10e-5) for the current partition, and if it results in a positive contribution to the Modularity. Given an initial separation using the leading eigen step, 'rSpectral' then continues to maximise for the change in Modularity using a fine-tuning step - or variate thereof. The first stage here is to find the node which, when moved from one community to another, gives the maximum change in Modularity. This node
How to cite:
Anatoly Sorokin (2022). rSpectral: Spectral Modularity Clustering. R package version 1.0.0.10, https://cran.r-project.org/web/packages/rSpectral. Accessed 16 Apr. 2025.
Previous versions and publish date:
1.0.0.9 (2022-08-19 14:20)
Other packages that cited rSpectral R package
View rSpectral citation profile
Other R packages that rSpectral depends, imports, suggests or enhances
Complete documentation for rSpectral
Functions, R codes and Examples using the rSpectral R package
Some associated functions: rSpectral . spectral_graphNEL . spectral_igraph_communities . spectral_igraph_membership . 
Some associated R codes: RcppExports.R . graphNEL.R . igraph.R . rSpectral-package.R .  Full rSpectral package functions and examples
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