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

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.

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**

```
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)
```

```
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")
```

In this example, the Louvain algorithm fits better the network of interest, as the curve of the stability measure varies less than the one obtained by the Fast greedy method.

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

ROBustness In Network (robin): an R package for Comparison and Validation of communities Valeria Policastro, Dario Righelli, Annamaria Carissimo, Luisa Cutillo, Italia De Feis. The R Journal (2021) https://journal.r-project.org/archive/2021/RJ-2021-040/index.html

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

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