robin

Available on CRAN https://CRAN.R-project.org/package=robin

ROBIN (ROBustness In Network) is an R package for the validation of community detection. It has a double aim: it studies the robustness of a community detection algorithm and it compares the robustness of two community detection algorithms.

The package implements a methodology that detects if the community structure found by a detection algorithm is statistically significant or is a result of chance, merely due to edge positions in the network.

The package:

1. Examine the robustness of a community detection algorithm against random perturbations of the original graph

2. Tests the statistical difference between the stability measure curves created

3. Makes a comparison between different community detection algorithms to choose the one that better fits the network of interest

4. Gives a graphical interactive representation

Example 1: "Robustness of a community detection algorithm"

my_network <- system.file("example/football.gml", package="robin")
graph <- prepGraph(file=my_network, file.format="gml")
graphRandom <- random(graph=graph)
proc <- robinRobust(graph=graph, graphRandom=graphRandom, measure="vi", 
                  method="louvain", type="independent")               
plotRobin(graph=graph, model1=proc$Mean, model2=proc$MeanRandom, 
legend=c("real data", "null model"), measure="vi")

#For the testing:
robinFDATest(graph=graph, model1=proc$Mean, model2=proc$MeanRandom, 
             measure="vi")
robinGPTest(model1=proc$Mean, model2=proc$MeanRandom)

Example 2: "Comparison of two community detection algorithms"

my_network <- system.file("example/football.gml", package="robin")
graph <- prepGraph(file=my_network, file.format="gml")
comp <- robinCompare(graph=graph, method1="fastGreedy",
                method2="louvain", measure="vi", type="independent")                
plotRobin(graph=graph, model1=comp$Mean1, model2=comp$Mean2, measure="vi", 
legend=c("fastGreedy", "louvain"), title="FastGreedy vs Louvain")

#For the testing:
robinFDATest(graph=graph, model1=comp$Mean1, model2=comp$Mean2, measure="vi")
robinGPTest(model1=comp$Mean1, model2=comp$Mean2)

License

Copyright (c) 2019 V. Policastro, A. Carissimo, L. Cutillo, I. De Feis and D. Righelli.