DeepAI AI Chat
Log In Sign Up

ROBustness In Network (robin): an R package for Comparison and Validation of communities

by   Valeria Policastro, et al.

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.


page 3

page 4

page 12

page 14

page 15


DynComm R Package -- Dynamic Community Detection for Evolving Networks

Nowadays, the analysis of dynamics in networks represents a great deal i...

An inferential procedure for community structure validation in networks

`Community structure' is a commonly observed feature of real networks. T...

Certified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing

Community detection plays a key role in understanding graph structure. H...

Package Theft Detection from Smart Home Security Cameras

Package theft detection has been a challenging task mainly due to lack o...

Robustness modularity in complex networks

A basic question in network community detection is how modular a given n...

Data Validation Infrastructure for R

Checking data quality against domain knowledge is a common activity that...

Evaluating Community Detection Algorithms for Progressively Evolving Graphs

Many algorithms have been proposed in the last ten years for the discove...