Quantifying layer similarity in multiplex networks: a systematic study

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.

Interacting Spreading Processes in Multilayer Networks: A Systematic Review

“…Out of all analysed papers only five (9%) of them is using full multilayer networks [69] [115] [78] [89] [101]. This shows than the vast majority of the studies considers less complex case -multiplex networks, or to be more specific node-aligned multiplex networks [117] [124]. In Table 6 we present which studies used which network type when modelling the multispread.…”

A Twitter-Based Study of the European Internet of Things

“…This is tested in Fig. 12, showing the coverage (Bródka et al 2018) for the actors on the various layers, that is, what percentage of actors in a layer is also present in another, for each ordered pair of layers. As it can be seen by inspecting the rows Application areas and Technologies and Techniques, containing high values of coverage across the whole row, many of the users participating in discussions on other topics are subsets of the users participating in these two topics.…”

Country centrality in the international multiplex network

“…In recent years multilayer networks have become the subject of several scholarly works aimed at generalizing network statistics and algorithms from single to multiple layer analysis (Lee et al 2012;De Domenico et al 2015a;Kivela et al 2014;Battiston et al 2014;Boccaletti et al 2014;Bródka et al 2018;De Bacco et al 2017;Aleta and Moreno 2019).…”

“…Given that our interest is in the aggregated multiplex and not in the single layers composing it, we have simplified the presentation of the single layer analysis by computing network statistics on the unweighted version of our graphs, without taking into consideration those assortativity and clustering measures which rely on the directionality of edges. We also do not perform a full fledged analysis of similarity of layers, as in Bródka et al (2018). Table 2 reports the network statistics for the cross section in 2003 of our multiplex.…”