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

Valeria Policastro,Dario Righelli,Luisa Cutillo,Annamaria Carissimo,Italia,De Feis

doi:10.32614/rj-2021-040

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.

Highlights

Over the last twenty years, network science has become a strategic field of research thanks to the strong development of high-performance computing technologies
It is based on a procedure that gives the opportunity to use the community detection techniques implemented in the igraph package Csardi and Nepusz (2019) while providing the user with the possibility to include other community detection algorithms. robin initially detects if the community structure found by some algorithms is statistically significant, it compares the different selected detection algorithms on the same network. robin assumes undirected graphs without loops and multiple edges
We presented robin, an R/Comprehensive R Archive Network (CRAN) package, to assess the robustness of the community structure of a network found by one or more detection methods, providing an indication of their reliability

Summary

Introduction

Over the last twenty years, network science has become a strategic field of research thanks to the strong development of high-performance computing technologies. Robin initially detects if the community structure found by some algorithms is statistically significant, it compares the different selected detection algorithms on the same network.

Results

Conclusion