<<<<<<< HEAD # 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.

**Examine the robustness**of a community detection algorithm against random perturbations of the original graph**Tests the statistical difference**between the stability measure curves createdMakes a

**comparison between different community detection algorithms**to choose the one that better fits the network of interestGives 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")
robinGPTest(ratio=proc$ratios)
```

```
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")
robinAUC(graph=graph, model1=comp$Mean1, model2=comp$Mean2, measure="vi")
```

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.

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

======= # 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.

**Examine the robustness**of a community detection algorithm against random perturbations of the original graph**Tests the statistical difference**between the stability measure curves createdMakes a

**comparison between different community detection algorithms**to choose the one that better fits the network of interestGives 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")
robinGPTest(ratio=proc$ratios)
```

```
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")
robinAUC(graph=graph, model1=comp$Mean1, model2=comp$Mean2, measure="vi")
```

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.

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

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