# 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:

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

**Tests the statistical difference** between the
stability measure curves created

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

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, method="louvain")
plot(proc)
```

```
#For the testing:
robinFDATest(proc)
robinGPTest(proc)
```

```
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")
plot(comp)
```

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. Lower the curve more stable is the
community detection method.

```
#For the testing:
robinFDATest(comp)
robinGPTest(comp)
```

## Reference

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

## License

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