ORIGINAL RESEARCH
published: 18 August 2015
doi: 10.3389/fphy.2015.00061
Frontiers in Physics | www.frontiersin.org 1 A
ugust 2015 | Volume 3 | Article 61
Edited by:
Taha Yasseri,
University of Oxford, UK
Reviewed by:
Renaud Lambiotte,
University of Namur, Belgium
Nuno A. M. Araújo,
Universidade de Lisboa, Portugal
*Correspondence:
Albert Solé-Ribalta,
Departament d’Enginyeria Informàtica
i Matemàtiques, Universitat Rovira i
Virgili, Avinguda Països Catalans 26,
43007 Tarragona, Spain
albert.sole@urv.cat
Specialty section:
This article was submitted to
Interdisciplinary Physics,
a section of the journal
Frontiers in Physics
Received: 30 June 2015
Accepted: 28 July 2015
Published: 18 August 2015
Citation:
Solé-Ribalta A, Granell C, Gómez S
and Arenas A (2015) Information
transfer in community structured
multiplex networks. Front. Phys. 3:61.
doi: 10.3389/fphy.2015.00061
Information transfer in community
structured multiplex networks
Albert Solé-Ribalta
*
, Clara Granell, Sergio Gómez and Alex Arenas
Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain
The study of complex networks that account for different types of interactions has
become a subject of interest in the last few years, specially because its representational
power in the description of users interactions in diverse online social platforms (Facebook,
Twitter, Instagram, etc.). The mathematical description of these interacting networks
has been coined under the name of multilayer networks, where each layer accounts
for a type of interaction. It has been shown that diffusive processes on top of these
networks present a phenomenology that cannot be explained by the naive superposition
of single layer diffusive phenomena but require the whole structure of interconnected
layers. Nevertheless, the description of diffusive phenomena on multilayer networks has
obviated the fact that social networks have strong mesoscopic structure represented
by different communities of individuals driven by common interests, or any other social
aspect. In this work, we study the transfer of information in multilayer networks with
community structure. The final goal is to understand and quantify, if the existence of
well-defined community structure at the level of individual layers, together with the
multilayer structure of the whole network, enhances or deteriorates the diffusion of
packets of information.
Keywords: complex networks, information diffusion, multivariate analysis, community structure, centrality
1. Introduction
The study of transport properties of networks is becoming increasingly important due to the
constantly growing amount of information and commodities being transferred through them. A
particular focus of these studies is how to make the c apacity of the diffusion of information in
the network maximal while minimizing the delivery time. In the basic approach information is
formed by units, the “packets,” and the handling of information for processing and distribution
takes finite time. Bot h network packet routing strategies and network topology play an essential role
in networks’ traffic flow. In realistic settings, like online social networks, the knowledge that any one
has about the topology of the network is limited to its local area of influence. Consequently, much
of the focus in recent studies has been on “searchability,” the process of sending information to a
target when the trajectory to reach the target is unknown. Moreover, given the limited capability of
nodes to handle information packets and redistribute them, the problem of congestion arises [1–
3]. It has been observed, both in real world networks and in model communication networks, that
the network flow collapses when the load (number of packets to be processed) is above a certain
threshold [3].
In general, most real and engineered systems include multiple subsystems and layers of
connectivity, and it is important to take such features into account when trying to obtain a
complete understanding of them. It is thus necessary to generalize the “traditional” network theory