6,185 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
Worm Epidemics in Wireless Adhoc Networks
A dramatic increase in the number of computing devices with wireless
communication capability has resulted in the emergence of a new class of
computer worms which specifically target such devices. The most striking
feature of these worms is that they do not require Internet connectivity for
their propagation but can spread directly from device to device using a
short-range radio communication technology, such as WiFi or Bluetooth. In this
paper, we develop a new model for epidemic spreading of these worms and
investigate their spreading in wireless ad hoc networks via extensive Monte
Carlo simulations. Our studies show that the threshold behaviour and dynamics
of worm epidemics in these networks are greatly affected by a combination of
spatial and temporal correlations which characterize these networks, and are
significantly different from the previously studied epidemics in the Internet
Transmission of severe acute respiratory syndrome in dynamical small-world networks
The outbreak of severe acute respiratory syndrome (SARS) is still threatening
the world because of a possible resurgence. In the current situation that
effective medical treatments such as antiviral drugs are not discovered yet,
dynamical features of the epidemics should be clarified for establishing
strategies for tracing, quarantine, isolation, and regulating social behavior
of the public at appropriate costs. Here we propose a network model for SARS
epidemics and discuss why superspreaders emerged and why SARS spread especially
in hospitals, which were key factors of the recent outbreak. We suggest that
superspreaders are biologically contagious patients, and they may amplify the
spreads by going to potentially contagious places such as hospitals. To avoid
mass transmission in hospitals, it may be a good measure to treat suspected
cases without hospitalizing them. Finally, we indicate that SARS probably
propagates in small-world networks associated with human contacts and that the
biological nature of individuals and social group properties are factors more
important than the heterogeneous rates of social contacts among individuals.
This is in marked contrast with epidemics of sexually transmitted diseases or
computer viruses to which scale-free network models often apply.Comment: 4 figure
The Evolutionary Price of Anarchy: Locally Bounded Agents in a Dynamic Virus Game
The Price of Anarchy (PoA) is a well-established game-theoretic concept to shed light on coordination issues arising in open distributed systems. Leaving agents to selfishly optimize comes with the risk of ending up in sub-optimal states (in terms of performance and/or costs), compared to a centralized system design. However, the PoA relies on strong assumptions about agents\u27 rationality (e.g., resources and information) and interactions, whereas in many distributed systems agents interact locally with bounded resources. They do so repeatedly over time (in contrast to "one-shot games"), and their strategies may evolve.
Using a more realistic evolutionary game model, this paper introduces a realized evolutionary Price of Anarchy (ePoA). The ePoA allows an exploration of equilibrium selection in dynamic distributed systems with multiple equilibria, based on local interactions of simple memoryless agents.
Considering a fundamental game related to virus propagation on networks, we present analytical bounds on the ePoA in basic network topologies and for different strategy update dynamics. In particular, deriving stationary distributions of the stochastic evolutionary process, we find that the Nash equilibria are not always the most abundant states, and that different processes can feature significant off-equilibrium behavior, leading to a significantly higher ePoA compared to the PoA studied traditionally in the literature
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