Correlation analysis of node and edge centrality measures in artificial complex networks

TL;DR

This paper investigates the correlation between traditional and alternative centrality measures in artificial complex networks, suggesting that computationally affordable measures can effectively replace classical ones.

Contribution

It introduces and analyzes the correlation between WERW-Kpath, Game of Thieves, and classical centrality measures, proposing more efficient alternatives.

Findings

Strong correlation between WERW-Kpath and classical measures

Strong correlation between Game of Thieves and classical measures

Potential for replacing classical measures with more affordable ones

Abstract

The importance of a node in a social network is identified through a set of measures called centrality. Degree centrality, closeness centrality, betweenness centrality and clustering coefficient are the most frequently used metrics to compute node centrality. Their computational complexity in some cases makes unfeasible, when not practically impossible, their computations. For this reason we focused on two alternative measures, WERW-Kpath and Game of Thieves, which are at the same time highly descriptive and computationally affordable. Our experiments show that a strong correlation exists between WERW-Kpath and Game of Thieves and the classical centrality measures. This may suggest the possibility of using them as useful and more economic replacements of the classical centrality measures.