ClassicalAndCommunityAwareCentralityAnalysis

This repository contains the code to conduct the analysis of the article: Rajeh, S., Savonnet, M., Leclercq, E. et al. Characterizing the interactions between classical and community-aware centrality measures in complex networks. Sci Rep 11, 10088 (2021). https://doi.org/10.1038/s41598-021-89549-x

Comments and questions are welcome, contact: stephany.rajeh(at)u-bourgogne.fr

The sources of datasets used in the study are available within the article.

CentralityCalculationAndFurtherProcessing

1. Calculates the classical and community-aware centrality for a given network.
2. Computes the correlation for all possible combinations between classical and community-aware centrality measures are then represent them in a heatmap.

Note: Classical measures are already written with networkx in Python and centiserve in R while community-aware centrality measures are written for the study of this paper.

NetworkTopologyCharacteristicsExtraction

1. Extracts macroscopic features for a given network
2. Extracts mesoscopic features for a given network

Note: Code for macroscopic features is already written with networkx in Python while code for mesoscopic features are written for the study of this paper.

Linear Regression

1. Code for performing linear regression using ordinary least squares (in Python)
2. Code for performing linear regression using weighted least squares (in R)

Note: Linear regression concering the Degree Distribution Exponent is calculated excluding the Football network, in a separate folder.