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README

The goal of ‘rSpectral’ is to make Spectral Modularity graph clustering method available to most of R graph frameworks.

You can install the development version of rSpectral from GitHub with:

# install.packages("devtools")
devtools::install_github("cmclean5/rSpectral")

This is a basic example which shows you how to solve a common problem

1. load the karate club graph and plot Faction membership:

library(rSpectral)
library(igraph)
#> 
#> Attaching package: 'igraph'
#> The following objects are masked from 'package:stats':
#> 
#>     decompose, spectrum
#> The following object is masked from 'package:base':
#> 
#>     union
data(karate, package="igraphdata")
l<-layout_nicely(karate)
memT<-V(karate)$Faction
palette <- rainbow(max(as.numeric(memT)))
plot(karate,vertex.color=palette[memT],layout=l)

1. run spectral clustering on graph

mem0<-igraph::membership(rSpectral::spectral_igraph_communities(karate))

1. plot the graph with membership colors

palette <- rainbow(max(as.numeric(mem0)))
plot(karate,vertex.color=palette[mem0],layout=l)

1. run spectral clustering on graph, fixing neighbouring nodes found in same community

mem1<-igraph::membership(
  rSpectral::spectral_igraph_communities(karate, fix_neig=1))

1. and plot again

palette <- rainbow(max(as.numeric(mem1)))
plot(karate,vertex.color=palette[mem1],layout=l)

1. run spectral clustering - fixing neighbouring nodes, Cnmin=5

mem1.5<-igraph::membership(
  rSpectral::spectral_igraph_communities(karate, fix_neig=1,Cn_min=5))

1. and the last plot

palette <- rainbow(max(as.numeric(mem1.5)))
plot(karate,vertex.color=palette[mem1.5],layout=l)

GraphNEL objects could be processed similarily, all other graph types could be converted either to igraph or to GraphNEL by packages such as Intergraph.

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