Similarity matrix average for aggregating multiplex networks

TL;DR

This paper presents a new method for aggregating multiplex networks into a single network by averaging similarity matrices, demonstrated on a network of statistical journals based on various similarity measures.

Contribution

The paper introduces a novel similarity matrix averaging approach for multiplex network aggregation, with a practical implementation and real-world application.

Findings

Effective aggregation of multiplex layers into a monoplex network.

Application to statistical journals shows meaningful similarity integration.

Method provides a theoretical and practical framework for multiplex network analysis.

Abstract

We introduce a methodology based on averaging similarity matrices with the aim of integrating the layers of a multiplex network into a single monoplex network. Multiplex networks are adopted for modelling a wide variety of real-world frameworks, such as multi-type relations in social, economic and biological structures. More specifically, multiplex networks are used when relations of different nature (layers) arise between a set of elements from a given population (nodes). A possible approach for investigating multiplex networks consists in aggregating the different layers in a single network (monoplex) which is a valid representation -- in some sense -- of all the layers. In order to obtain such an aggregated network, we propose a theoretical approach -- along with its practical implementation -- which stems on the concept of similarity matrix average. This methodology is finally applied to a multiplex similarity network of statistical journals, where the three considered layers express the similarity of the journals based on co-citations, common authors and common editors, respectively.