59,553 research outputs found
Quarantine generated phase transition in epidemic spreading
We study the critical effect of quarantine on the propagation of epidemics on
an adaptive network of social contacts. For this purpose, we analyze the
susceptible-infected-recovered (SIR) model in the presence of quarantine, where
susceptible individuals protect themselves by disconnecting their links to
infected neighbors with probability w, and reconnecting them to other
susceptible individuals chosen at random. Starting from a single infected
individual, we show by an analytical approach and simulations that there is a
phase transition at a critical rewiring (quarantine) threshold w_c separating a
phase (w<w_c) where the disease reaches a large fraction of the population,
from a phase (w >= w_c) where the disease does not spread out. We find that in
our model the topology of the network strongly affects the size of the
propagation, and that w_c increases with the mean degree and heterogeneity of
the network. We also find that w_c is reduced if we perform a preferential
rewiring, in which the rewiring probability is proportional to the degree of
infected nodes.Comment: 13 pages, 6 figure
Dynamics in online social networks
An increasing number of today's social interactions occurs using online
social media as communication channels. Some online social networks have become
extremely popular in the last decade. They differ among themselves in the
character of the service they provide to online users. For instance, Facebook
can be seen mainly as a platform for keeping in touch with close friends and
relatives, Twitter is used to propagate and receive news, LinkedIn facilitates
the maintenance of professional contacts, Flickr gathers amateurs and
professionals of photography, etc. Albeit different, all these online platforms
share an ingredient that pervades all their applications. There exists an
underlying social network that allows their users to keep in touch with each
other and helps to engage them in common activities or interactions leading to
a better fulfillment of the service's purposes. This is the reason why these
platforms share a good number of functionalities, e.g., personal communication
channels, broadcasted status updates, easy one-step information sharing, news
feeds exposing broadcasted content, etc. As a result, online social networks
are an interesting field to study an online social behavior that seems to be
generic among the different online services. Since at the bottom of these
services lays a network of declared relations and the basic interactions in
these platforms tend to be pairwise, a natural methodology for studying these
systems is provided by network science. In this chapter we describe some of the
results of research studies on the structure, dynamics and social activity in
online social networks. We present them in the interdisciplinary context of
network science, sociological studies and computer science.Comment: 17 pages, 4 figures, book chapte
Dynamical patterns of epidemic outbreaks in complex heterogeneous networks
We present a thorough inspection of the dynamical behavior of epidemic
phenomena in populations with complex and heterogeneous connectivity patterns.
We show that the growth of the epidemic prevalence is virtually instantaneous
in all networks characterized by diverging degree fluctuations, independently
of the structure of the connectivity correlation functions characterizing the
population network. By means of analytical and numerical results, we show that
the outbreak time evolution follows a precise hierarchical dynamics. Once
reached the most highly connected hubs, the infection pervades the network in a
progressive cascade across smaller degree classes. Finally, we show the
influence of the initial conditions and the relevance of statistical results in
single case studies concerning heterogeneous networks. The emerging theoretical
framework appears of general interest in view of the recently observed
abundance of natural networks with complex topological features and might
provide useful insights for the development of adaptive strategies aimed at
epidemic containment.Comment: 13 pages, 11 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
The Impact of Social Curiosity on Information Spreading on Networks
Most information spreading models consider that all individuals are identical
psychologically. They ignore, for instance, the curiosity level of people,
which may indicate that they can be influenced to seek for information given
their interest. For example, the game Pok\'emon GO spread rapidly because of
the aroused curiosity among users. This paper proposes an information
propagation model considering the curiosity level of each individual, which is
a dynamical parameter that evolves over time. We evaluate the efficiency of our
model in contrast to traditional information propagation models, like SIR or
IC, and perform analysis on different types of artificial and real-world
networks, like Google+, Facebook, and the United States roads map. We present a
mean-field approach that reproduces with a good accuracy the evolution of
macroscopic quantities, such as the density of stiflers, for the system's
behavior with the curiosity. We also obtain an analytical solution of the
mean-field equations that allows to predicts a transition from a phase where
the information remains confined to a small number of users to a phase where it
spreads over a large fraction of the population. The results indicate that the
curiosity increases the information spreading in all networks as compared with
the spreading without curiosity, and that this increase is larger in spatial
networks than in social networks. When the curiosity is taken into account, the
maximum number of informed individuals is reached close to the transition
point. Since curious people are more open to a new product, concepts, and
ideas, this is an important factor to be considered in propagation modeling.
Our results contribute to the understanding of the interplay between diffusion
process and dynamical heterogeneous transmission in social networks.Comment: 8 pages, 5 figure
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