Quantifying layer similarity in multiplex networks: a systematic study

TL;DR

This paper systematically evaluates methods for quantifying layer similarity in multiplex networks, providing a comprehensive taxonomy, extending existing approaches, and offering practical guidelines for their application.

Contribution

It introduces a detailed taxonomy of layer similarity measures, extends existing methods, and offers practical guidelines for their effective use in multiplex network analysis.

Findings

Developed a comprehensive taxonomy of similarity measures

Extended existing approaches with new methods

Provided practical guidelines for applying similarity measures

Abstract

Computing layer similarities is an important way of characterizing multiplex networks because various static properties and dynamic processes depend on the relationships between layers. We provide a taxonomy and experimental evaluation of approaches to compare layers in multiplex networks. Our taxonomy includes, systematizes and extends existing approaches, and is complemented by a set of practical guidelines on how to apply them.