Robust classification of salient links in complex networks

TL;DR

This paper demonstrates that link salience can robustly classify network elements into meaningful groups across various complex networks without external parameters, revealing universal structural features.

Contribution

It introduces link salience as a parameter-free method for classifying network links, showing its effectiveness across diverse empirical networks.

Findings

Link salience classifies network links into distinct groups.

Salient skeletons exhibit universal statistical properties.

Salience predicts contagion dynamics on networks.

Abstract

Complex networks in natural, social, and technological systems generically exhibit an abundance of rich information. Extracting meaningful structural features from data is one of the most challenging tasks in network theory. Many methods and concepts have been proposed to address this problem such as centrality statistics, motifs, community clusters, and backbones, but such schemes typically rely on external and arbitrary parameters. It is unknown whether generic networks permit the classification of elements without external intervention. Here we show that link salience is a robust approach to classifying network elements based on a consensus estimate of all nodes. A wide range of empirical networks exhibit a natural, network-implicit classification of links into qualitatively distinct groups, and the salient skeletons have generic statistical properties. Salience also predicts essential features of contagion phenomena on networks, and points towards a better understanding of universal features in empirical networks that are masked by their complexity.