ROBustness In Network (robin): an R Package for Comparison and Validation of Communities

In network analysis, many community detection algorithms have been developed. However, their implementation leaves unaddressed the question of the statistical validation of the results. Here, we present robin (ROBustness In Network), an R package to assess the robustness of the community structure of a network found by one or more methods to give indications about their reliability. The procedure initially detects if the community structure found by a set of algorithms is statistically significant and then compares two selected detection algorithms on the same graph to choose the one that better fits the network of interest. We demonstrate the use of our package on the American College Football benchmark dataset.

Valeria Policastro , Dario Righelli , Annamaria Carissimo , Luisa Cutillo , Italia De Feis
2020-06-03

CRAN packages used

robin, igraph, networkD3, ggplot2, gridExtra, fdatest, DescTools

CRAN Task Views implied by cited packages

Graphics, FunctionalData, gR, MissingData, Optimization, Phylogenetics, Spatial, TeachingStatistics

Bioconductor packages used

gprege

Reuse

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Citation

For attribution, please cite this work as

Policastro, et al., "The R Journal: ROBustness In Network (robin): an R Package for Comparison and Validation of Communities ", {The R Journal}, 2020

BibTeX citation

@article{RJ-2021-040,
  author = {Policastro, Valeria and Righelli, Dario and Carissimo, Annamaria and Cutillo, Luisa and Feis, Italia De},
  title = {The R Journal: ROBustness In Network (robin): an R Package for Comparison and Validation of Communities },
  journal = {{The R Journal}},
  year = {2020},
  note = {https://doi.org/10.32614/RJ-2021-040},
  doi = {10.32614/RJ-2021-040},
  volume = {13},
  issue = {1},
  issn = {2073-4859},
  pages = {292-309}
}