27,969 research outputs found
Opinion-Based Centrality in Multiplex Networks: A Convex Optimization Approach
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
Effects of Time Horizons on Influence Maximization in the Voter Dynamics
In this paper we analyze influence maximization in the voter model with an
active strategic and a passive influencing party in non-stationary settings. We
thus explore the dependence of optimal influence allocation on the time
horizons of the strategic influencer. We find that on undirected heterogeneous
networks, for short time horizons, influence is maximized when targeting
low-degree nodes, while for long time horizons influence maximization is
achieved when controlling hub nodes. Furthermore, we show that for short and
intermediate time scales influence maximization can exploit knowledge of
(transient) opinion configurations. More in detail, we find two rules. First,
nodes with states differing from the strategic influencer's goal should be
targeted. Second, if only few nodes are initially aligned with the strategic
influencer, nodes subject to opposing influence should be avoided, but when
many nodes are aligned, an optimal influencer should shadow opposing influence.Comment: 22 page
- …