2,805 research outputs found
Activation thresholds in epidemic spreading with motile infectious agents on scale-free networks
We investigate a fermionic susceptible-infected-susceptible model with
mobility of infected individuals on uncorrelated scale-free networks with
power-law degree distributions of exponents
. Two diffusive processes with diffusion rate of an infected
vertex are considered. In the \textit{standard diffusion}, one of the
nearest-neighbors is chosen with equal chance while in the \textit{biased
diffusion} this choice happens with probability proportional to the neighbor's
degree. A non-monotonic dependence of the epidemic threshold on with an
optimum diffusion rate , for which the epidemic spreading is more
efficient, is found for standard diffusion while monotonic decays are observed
in the biased case. The epidemic thresholds go to zero as the network size is
increased and the form that this happens depends on the diffusion rule and
degree exponent. We analytically investigated the dynamics using quenched and
heterogeneous mean-field theories. The former presents, in general, a better
performance for standard and the latter for biased diffusion models, indicating
different activation mechanisms of the epidemic phases that are rationalized in
terms of hubs or max -core subgraphs.Comment: 9 pages, 4 figure
Description of spreading dynamics by microscopic network models and macroscopic branching processes can differ due to coalescence
Spreading processes are conventionally monitored on a macroscopic level by
counting the number of incidences over time. The spreading process can then be
modeled either on the microscopic level, assuming an underlying interaction
network, or directly on the macroscopic level, assuming that microscopic
contributions are negligible. The macroscopic characteristics of both
descriptions are commonly assumed to be identical. In this work, we show that
these characteristics of microscopic and macroscopic descriptions can be
different due to coalescence, i.e., a node being activated at the same time by
multiple sources. In particular, we consider a (microscopic) branching network
(probabilistic cellular automaton) with annealed connectivity disorder, record
the macroscopic activity, and then approximate this activity by a (macroscopic)
branching process. In this framework, we analytically calculate the effect of
coalescence on the collective dynamics. We show that coalescence leads to a
universal non-linear scaling function for the conditional expectation value of
successive network activity. This allows us to quantify the difference between
the microscopic model parameter and established macroscopic estimates. To
overcome this difference, we propose a non-linear estimator that correctly
infers the model branching parameter for all system sizes.Comment: 13 page
Anomalous transport in complex networks
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 43-45).The emergence of scaling in transport through interconnected systems is a consequence of the topological structure of the network and the physical mechanisms underlying the transport dynamics. We study transport by advection and diffusion in scale-free and Erdős-Rényi networks. Using stochastic particle simulations, we find anomalous (nonlinear) scaling of the mean square displacement with time. We show the connection with existing descriptions of anomalous transport in disordered systems, and explain the mean transport behavior from the coupled nature of particle jump lengths and transition times. Moreover, we study epidemic spreading through the air transportation network with a particle-tracking model that accounts for the spatial distribution of airports, detailed air traffic and realistic (correlated) waitingtime distributions of individual agents. We use empirical data from US air travel to constrain the model parameters and validate the model's predictions of traffic patterns. We formulate a theory that identifies the most influential spreaders from the point of view of early-time spreading behavior. We find that network topology, geography, aggregate traffic and individual mobility patterns are all essential for accurate predictions of spreading.by Christos Nicolaides.S.M
Organic Design of Massively Distributed Systems: A Complex Networks Perspective
The vision of Organic Computing addresses challenges that arise in the design
of future information systems that are comprised of numerous, heterogeneous,
resource-constrained and error-prone components or devices. Here, the notion
organic particularly highlights the idea that, in order to be manageable, such
systems should exhibit self-organization, self-adaptation and self-healing
characteristics similar to those of biological systems. In recent years, the
principles underlying many of the interesting characteristics of natural
systems have been investigated from the perspective of complex systems science,
particularly using the conceptual framework of statistical physics and
statistical mechanics. In this article, we review some of the interesting
relations between statistical physics and networked systems and discuss
applications in the engineering of organic networked computing systems with
predictable, quantifiable and controllable self-* properties.Comment: 17 pages, 14 figures, preprint of submission to Informatik-Spektrum
published by Springe
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