551 research outputs found
Suppressing disease spreading by using information diffusion on multiplex networks
Although there is always an interplay between the dynamics of information
diffusion and disease spreading, the empirical research on the systemic
coevolution mechanisms connecting these two spreading dynamics is still
lacking. Here we investigate the coevolution mechanisms and dynamics between
information and disease spreading by utilizing real data and a proposed
spreading model on multiplex network. Our empirical analysis finds asymmetrical
interactions between the information and disease spreading dynamics. Our
results obtained from both the theoretical framework and extensive stochastic
numerical simulations suggest that an information outbreak can be triggered in
a communication network by its own spreading dynamics or by a disease outbreak
on a contact network, but that the disease threshold is not affected by
information spreading. Our key finding is that there is an optimal information
transmission rate that markedly suppresses the disease spreading. We find that
the time evolution of the dynamics in the proposed model qualitatively agrees
with the real-world spreading processes at the optimal information transmission
rate.Comment: 11 pages, 8 figure
Spreading processes in Multilayer Networks
Several systems can be modeled as sets of interconnected networks or networks
with multiple types of connections, here generally called multilayer networks.
Spreading processes such as information propagation among users of an online
social networks, or the diffusion of pathogens among individuals through their
contact network, are fundamental phenomena occurring in these networks.
However, while information diffusion in single networks has received
considerable attention from various disciplines for over a decade, spreading
processes in multilayer networks is still a young research area presenting many
challenging research issues. In this paper we review the main models, results
and applications of multilayer spreading processes and discuss some promising
research directions.Comment: 21 pages, 3 figures, 4 table
Asymmetrically interacting spreading dynamics on complex layered networks
The spread of disease through a physical-contact network and the spread of
information about the disease on a communication network are two intimately
related dynamical processes. We investigate the asymmetrical interplay between
the two types of spreading dynamics, each occurring on its own layer, by
focusing on the two fundamental quantities underlying any spreading process:
epidemic threshold and the final infection ratio. We find that an epidemic
outbreak on the contact layer can induce an outbreak on the communication
layer, and information spreading can effectively raise the epidemic threshold.
When structural correlation exists between the two layers, the information
threshold remains unchanged but the epidemic threshold can be enhanced, making
the contact layer more resilient to epidemic outbreak. We develop a physical
theory to understand the intricate interplay between the two types of spreading
dynamics.Comment: 29 pages, 14 figure
Interacting Spreading Processes in Multilayer Networks: A Systematic Review
© 2013 IEEE. The world of network science is fascinating and filled with complex phenomena that we aspire to understand. One of them is the dynamics of spreading processes over complex networked structures. Building the knowledge-base in the field where we can face more than one spreading process propagating over a network that has more than one layer is a challenging task, as the complexity comes both from the environment in which the spread happens and from characteristics and interplay of spreads' propagation. As this cross-disciplinary field bringing together computer science, network science, biology and physics has rapidly grown over the last decade, there is a need to comprehensively review the current state-of-the-art and offer to the research community a roadmap that helps to organise the future research in this area. Thus, this survey is a first attempt to present the current landscape of the multi-processes spread over multilayer networks and to suggest the potential ways forward
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
Epidemic spreading and risk perception in multiplex networks: a self-organized percolation method
In this paper we study the interplay between epidemic spreading and risk
perception on multiplex networks. The basic idea is that the effective
infection probability is affected by the perception of the risk of being
infected, which we assume to be related to the fraction of infected neighbours,
as introduced by Bagnoli et al., PRE 76:061904 (2007). We re-derive previous
results using a self-organized method, that automatically gives the percolation
threshold in just one simulation. We then extend the model to multiplex
networks considering that people get infected by contacts in real life but
often gather information from an information networks, that may be quite
different from the real ones. The similarity between the real and information
networks determine the possibility of stopping the infection for a sufficiently
high precaution level: if the networks are too different there is no mean of
avoiding the epidemics.Comment: 9 pages, 8 figure
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