The R Journal: article published in 2021, volume 13:1

ROBustness In Network (robin): an R Package for Comparison and Validation of Communities
Valeria Policastro, Dario Righelli, Annamaria Carissimo, Luisa Cutillo and Italia De Feis , The R Journal (2021) 13:1, pages 292-309.

Abstract 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.

Received: 2020-06-03; online 2021-06-07
CRAN packages: robin, igraph, networkD3, ggplot2, gridExtra, fdatest, DescTools
CRAN Task Views implied by cited CRAN packages: Graphics, FunctionalData, gR, MissingData, Optimization, Phylogenetics, Spatial, TeachingStatistics
Bioconductor packages: gprege

This article is licensed under a Creative Commons Attribution 4.0 International license.

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