2,834 research outputs found
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
Dissemination of Health Information within Social Networks
In this paper, we investigate, how information about a common food born
health hazard, known as Campylobacter, spreads once it was delivered to a
random sample of individuals in France. The central question addressed here is
how individual characteristics and the various aspects of social network
influence the spread of information. A key claim of our paper is that
information diffusion processes occur in a patterned network of social ties of
heterogeneous actors. Our percolation models show that the characteristics of
the recipients of the information matter as much if not more than the
characteristics of the sender of the information in deciding whether the
information will be transmitted through a particular tie. We also found that at
least for this particular advisory, it is not the perceived need of the
recipients for the information that matters but their general interest in the
topic
Emergence of Blind Areas in Information Spreading
Recently, contagion-based (disease, information, etc.) spreading on social
networks has been extensively studied. In this paper, other than traditional
full interaction, we propose a partial interaction based spreading model,
considering that the informed individuals would transmit information to only a
certain fraction of their neighbors due to the transmission ability in
real-world social networks. Simulation results on three representative networks
(BA, ER, WS) indicate that the spreading efficiency is highly correlated with
the network heterogeneity. In addition, a special phenomenon, namely
\emph{Information Blind Areas} where the network is separated by several
information-unreachable clusters, will emerge from the spreading process.
Furthermore, we also find that the size distribution of such information blind
areas obeys power-law-like distribution, which has very similar exponent with
that of site percolation. Detailed analyses show that the critical value is
decreasing along with the network heterogeneity for the spreading process,
which is complete the contrary to that of random selection. Moreover, the
critical value in the latter process is also larger that of the former for the
same network. Those findings might shed some lights in in-depth understanding
the effect of network properties on information spreading
A Self-Organized Method for Computing the Epidemic Threshold in Computer Networks
In many cases, tainted information in a computer network can spread in a way
similar to an epidemics in the human world. On the other had, information
processing paths are often redundant, so a single infection occurrence can be
easily "reabsorbed". Randomly checking the information with a central server is
equivalent to lowering the infection probability but with a certain cost (for
instance processing time), so it is important to quickly evaluate the epidemic
threshold for each node. We present a method for getting such information
without resorting to repeated simulations. As for human epidemics, the local
information about the infection level (risk perception) can be an important
factor, and we show that our method can be applied to this case, too. Finally,
when the process to be monitored is more complex and includes "disruptive
interference", one has to use actual simulations, which however can be carried
out "in parallel" for many possible infection probabilities
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