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Opinion-based centrality in multiplex networks: A convex optimization approach

Published online by Cambridge University Press:  06 June 2017

ALEXANDRE REIFFERS-MASSON  and
VINCENT LABATUT Open the ORCID record for VINCENT LABATUT [Opens in a new window]
ALEXANDRE REIFFERS-MASSON
Affiliation:
Laboratoire Informatique d'Avignon (LIA)EA 4128, Avignon, France (e-mails: reiffers.alexandre@gmail.com, vincent.labatut@univ-avignon.fr)
VINCENT LABATUT
Affiliation:
Laboratoire Informatique d'Avignon (LIA)EA 4128, Avignon, France (e-mails: reiffers.alexandre@gmail.com, vincent.labatut@univ-avignon.fr)
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Abstract

Most people simultaneously belong to several distinct social networks, in which their relations can be different. They have opinions about certain topics, which they share and spread on these networks, and are influenced by the opinions of other persons. In this paper, we build upon this observation to propose a new nodal centrality measure for multiplex networks. Our measure, called Opinion centrality, is based on a stochastic model representing opinion propagation dynamics in such a network. We formulate an optimization problem consisting in maximizing the opinion of the whole network when controlling an external influence able to affect each node individually. We find a mathematical closed form of this problem, and use its solution to derive our centrality measure. According to the opinion centrality, the more a node is worth investing external influence, and the more it is central. We perform an empirical study of the proposed centrality over a toy network, as well as a collection of real-world networks. Our measure is generally negatively correlated with existing multiplex centrality measures, and highlights different types of nodes, accordingly to its definition.

Keywords

complex networkscentrality measuresmultiplex networksopinion diffusionconvex optimization and stochastic process
Type
Research Article
Information
Network Science , Volume 5 , Special Issue 2: Modeling, Analysis, and Mining of Multilayer Networks , June 2017 , pp. 213 - 234
Copyright
Copyright © Cambridge University Press 2017 

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