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rSpectral

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

Installation

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

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

Example

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)

2. run spectral clustering on graph

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

3. plot the graph with membership colors

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

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

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

5. and plot again

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

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

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

5. 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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.