3,190 research outputs found
The influence of persuasion in opinion formation and polarization
We present a model that explores the influence of persuasion in a population
of agents with positive and negative opinion orientations. The opinion of each
agent is represented by an integer number that expresses its level of
agreement on a given issue, from totally against to totally in favor
. Same-orientation agents persuade each other with probability ,
becoming more extreme, while opposite-orientation agents become more moderate
as they reach a compromise with probability . The population initially
evolves to (a) a polarized state for , where opinions' distribution is
peaked at the extreme values , or (b) a centralized state for ,
with most opinions around . When , polarization lasts for a
time that diverges as , where is the population's size. Finally,
an extremist consensus ( or ) is reached in a time that scales as
for
Competition between surface relaxation and ballistic deposition models in scale free networks
In this paper we study the scaling behavior of the fluctuations in the steady
state with the system size for a surface growth process given by the
competition between the surface relaxation (SRM) and the Ballistic Deposition
(BD) models on degree uncorrelated Scale Free networks (SF), characterized by a
degree distribution , where is the degree of a node.
It is known that the fluctuations of the SRM model above the critical dimension
() scales logarithmically with on euclidean lattices. However,
Pastore y Piontti {\it et. al.} [A. L. Pastore y Piontti {\it et. al.}, Phys.
Rev. E {\bf 76}, 046117 (2007)] found that the fluctuations of the SRM model in
SF networks scale logarithmically with for and as a constant
for . In this letter we found that for a pure ballistic
deposition model on SF networks scales as a power law with an exponent
that depends on . On the other hand when both processes are in
competition, we find that there is a continuous crossover between a SRM
behavior and a power law behavior due to the BD model that depends on the
occurrence probability of each process and the system size. Interestingly, we
find that a relaxation process contaminated by any small contribution of
ballistic deposition will behave, for increasing system sizes, as a pure
ballistic one. Our findings could be relevant when surface relaxation
mechanisms are used to synchronize processes that evolve on top of complex
networks.Comment: 8 pages, 6 figure
Fluctuations of a surface relaxation model in interacting scale free networks
Isolated complex networks have been studied deeply in the last decades due to
the fact that many real systems can be modeled using these types of structures.
However, it is well known that the behavior of a system not only depends on
itself, but usually also depends on the dynamics of other structures. For this
reason, interacting complex networks and the processes developed on them have
been the focus of study of many researches in the last years. One of the most
studied subjects in this type of structures is the Synchronization problem,
which is important in a wide variety of processes in real systems. In this
manuscript we study the synchronization of two interacting scale-free networks,
in which each node has dependency links with different nodes in the other
network. We map the synchronization problem with an interface growth, by
studying the fluctuations in the steady state of a scalar field defined in both
networks.
We find that as slightly increases from , there is a really
significant decreasing in the fluctuations of the system. However, this
considerable improvement takes place mainly for small values of , when the
interaction between networks becomes stronger there is only a slight change in
the fluctuations. We characterize how the dispersion of the scalar field
depends on the internal degree, and we show that a combination between the
decreasing of this dispersion and the integer nature of our growth model are
the responsible for the behavior of the fluctuations with .Comment: 11 pages, 4 figures and 1 tabl
Interacting social processes on interconnected networks
We propose and study a model for the interplay between two different
dynamical processes --one for opinion formation and the other for decision
making-- on two interconnected networks and . The opinion dynamics on
network corresponds to that of the M-model, where the state of each agent
can take one of four possible values (), describing its level of
agreement on a given issue. The likelihood to become an extremist ()
or a moderate () is controlled by a reinforcement parameter .
The decision making dynamics on network is akin to that of the
Abrams-Strogatz model, where agents can be either in favor () or against
() the issue. The probability that an agent changes its state is
proportional to the fraction of neighbors that hold the opposite state raised
to a power . Starting from a polarized case scenario in which all agents
of network hold positive orientations while all agents of network have
a negative orientation, we explore the conditions under which one of the
dynamics prevails over the other, imposing its initial orientation. We find
that, for a given value of , the two-network system reaches a consensus
in the positive state (initial state of network ) when the reinforcement
overcomes a crossover value , while a negative consensus happens
for . In the phase space, the system displays a
transition at a critical threshold , from a coexistence of both
orientations for to a dominance of one orientation for
. We develop an analytical mean-field approach that gives an
insight into these regimes and shows that both dynamics are equivalent along
the crossover line .Comment: 25 pages, 6 figure
Recovery of Interdependent Networks
Recent network research has focused on the cascading failures in a system of
interdependent networks and the necessary preconditions for system collapse. An
important question that has not been addressed is how to repair a failing
system before it suffers total breakdown. Here we introduce a recovery strategy
of nodes and develop an analytic and numerical framework for studying the
concurrent failure and recovery of a system of interdependent networks based on
an efficient and practically reasonable strategy. Our strategy consists of
repairing a fraction of failed nodes, with probability of recovery ,
that are neighbors of the largest connected component of each constituent
network. We find that, for a given initial failure of a fraction of
nodes, there is a critical probability of recovery above which the cascade is
halted and the system fully restores to its initial state and below which the
system abruptly collapses. As a consequence we find in the plane of
the phase diagram three distinct phases. A phase in which the system never
collapses without being restored, another phase in which the recovery strategy
avoids the breakdown, and a phase in which even the repairing process cannot
avoid the system collapse
Synchronization in interacting Scale Free Networks
We study the fluctuations of the interface, in the steady state, of the
Surface Relaxation Model (SRM) in two scale free interacting networks where a
fraction of nodes in both networks interact one to one through external
connections. We find that as increases the fluctuations on both networks
decrease and thus the synchronization reaches an improvement of nearly
when . The decrease of the fluctuations on both networks is due mainly to
the diffusion through external connections which allows to reducing the load in
nodes by sending their excess mostly to low-degree nodes, which we report have
the lowest heights. This effect enhances the matching of the heights of low-and
high-degree nodes as increases reducing the fluctuations. This effect is
almost independent of the degree distribution of the networks which means that
the interconnection governs the behavior of the process over its topology.Comment: 13 pages, 7 figures. Added a relevant reference.Typos fixe
Evolution equation for a model of surface relaxation in complex networks
In this paper we derive analytically the evolution equation of the interface
for a model of surface growth with relaxation to the minimum (SRM) in complex
networks. We were inspired by the disagreement between the scaling results of
the steady state of the fluctuations between the discrete SRM model and the
Edward-Wilkinson process found in scale-free networks with degree distribution
for [Pastore y Piontti {\it et al.},
Phys. Rev. E {\bf 76}, 046117 (2007)]. Even though for Euclidean lattices the
evolution equation is linear, we find that in complex heterogeneous networks
non-linear terms appear due to the heterogeneity and the lack of symmetry of
the network; they produce a logarithmic divergency of the saturation roughness
with the system size as found by Pastore y Piontti {\it et al.} for .Comment: 9 pages, 2 figure
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