8,856 research outputs found
Renormalization group theory for percolation in time-varying networks
Motivated by multi-hop communication in unreliable wireless networks, we
present a percolation theory for time-varying networks. We develop a
renormalization group theory for a prototypical network on a regular grid,
where individual links switch stochastically between active and inactive
states. The question whether a given source node can communicate with a
destination node along paths of active links is equivalent to a percolation
problem. Our theory maps the temporal existence of multi-hop paths on an
effective two-state Markov process. We show analytically how this Markov
process converges towards a memory-less Bernoulli process as the hop distance
between source and destination node increases. Our work extends classical
percolation theory to the dynamic case and elucidates temporal correlations of
message losses. Quantification of temporal correlations has implications for
the design of wireless communication and control protocols, e.g. in
cyber-physical systems such as self-organized swarms of drones or smart traffic
networks.Comment: 8 pages, 3 figure
The Dynamics of Internet Traffic: Self-Similarity, Self-Organization, and Complex Phenomena
The Internet is the most complex system ever created in human history.
Therefore, its dynamics and traffic unsurprisingly take on a rich variety of
complex dynamics, self-organization, and other phenomena that have been
researched for years. This paper is a review of the complex dynamics of
Internet traffic. Departing from normal treatises, we will take a view from
both the network engineering and physics perspectives showing the strengths and
weaknesses as well as insights of both. In addition, many less covered
phenomena such as traffic oscillations, large-scale effects of worm traffic,
and comparisons of the Internet and biological models will be covered.Comment: 63 pages, 7 figures, 7 tables, submitted to Advances in Complex
System
A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems
In this paper we present a methodological framework that meets novel
requirements emerging from upcoming types of accelerated and highly
configurable neuromorphic hardware systems. We describe in detail a device with
45 million programmable and dynamic synapses that is currently under
development, and we sketch the conceptual challenges that arise from taking
this platform into operation. More specifically, we aim at the establishment of
this neuromorphic system as a flexible and neuroscientifically valuable
modeling tool that can be used by non-hardware-experts. We consider various
functional aspects to be crucial for this purpose, and we introduce a
consistent workflow with detailed descriptions of all involved modules that
implement the suggested steps: The integration of the hardware interface into
the simulator-independent model description language PyNN; a fully automated
translation between the PyNN domain and appropriate hardware configurations; an
executable specification of the future neuromorphic system that can be
seamlessly integrated into this biology-to-hardware mapping process as a test
bench for all software layers and possible hardware design modifications; an
evaluation scheme that deploys models from a dedicated benchmark library,
compares the results generated by virtual or prototype hardware devices with
reference software simulations and analyzes the differences. The integration of
these components into one hardware-software workflow provides an ecosystem for
ongoing preparative studies that support the hardware design process and
represents the basis for the maturity of the model-to-hardware mapping
software. The functionality and flexibility of the latter is proven with a
variety of experimental results
Modeling the Internet's Large-Scale Topology
Network generators that capture the Internet's large-scale topology are
crucial for the development of efficient routing protocols and modeling
Internet traffic. Our ability to design realistic generators is limited by the
incomplete understanding of the fundamental driving forces that affect the
Internet's evolution. By combining the most extensive data on the time
evolution, topology and physical layout of the Internet, we identify the
universal mechanisms that shape the Internet's router and autonomous system
level topology. We find that the physical layout of nodes form a fractal set,
determined by population density patterns around the globe. The placement of
links is driven by competition between preferential attachment and linear
distance dependence, a marked departure from the currently employed exponential
laws. The universal parameters that we extract significantly restrict the class
of potentially correct Internet models, and indicate that the networks created
by all available topology generators are significantly different from the
Internet
Modeling Structure and Resilience of the Dark Network
While the statistical and resilience properties of the Internet are no more
changing significantly across time, the Darknet, a network devoted to keep
anonymous its traffic, still experiences rapid changes to improve the security
of its users. Here, we study the structure of the Darknet and we find that its
topology is rather peculiar, being characterized by non-homogenous distribution
of connections -- typical of scale-free networks --, very short path lengths
and high clustering -- typical of small-world networks -- and lack of a core of
highly connected nodes.
We propose a model to reproduce such features, demonstrating that the
mechanisms used to improve cyber-security are responsible for the observed
topology. Unexpectedly, we reveal that its peculiar structure makes the Darknet
much more resilient than the Internet -- used as a benchmark for comparison at
a descriptive level -- to random failures, targeted attacks and cascade
failures, as a result of adaptive changes in response to the attempts of
dismantling the network across time.Comment: 8 pages, 5 figure
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
Navigability of temporal networks in hyperbolic space
Information routing is one of the main tasks in many complex networks with a
communication function. Maps produced by embedding the networks in hyperbolic
space can assist this task enabling the implementation of efficient navigation
strategies. However, only static maps have been considered so far, while
navigation in more realistic situations, where the network structure may vary
in time, remain largely unexplored. Here, we analyze the navigability of real
networks by using greedy routing in hyperbolic space, where the nodes are
subject to a stochastic activation-inactivation dynamics. We find that such
dynamics enhances navigability with respect to the static case. Interestingly,
there exists an optimal intermediate activation value, which ensures the best
trade-off between the increase in the number of successful paths and a limited
growth of their length. Contrary to expectations, the enhanced navigability is
robust even when the most connected nodes inactivate with very high
probability. Finally, our results indicate that some real networks are
ultranavigable and remain highly navigable even if the network structure is
extremely unsteady. These findings have important implications for the design
and evaluation of efficient routing protocols that account for the temporal
nature of real complex networks.Comment: 10 pages, 4 figures. Includes Supplemental Informatio
Hidden geometric correlations in real multiplex networks
Real networks often form interacting parts of larger and more complex
systems. Examples can be found in different domains, ranging from the Internet
to structural and functional brain networks. Here, we show that these multiplex
systems are not random combinations of single network layers. Instead, they are
organized in specific ways dictated by hidden geometric correlations between
the individual layers. We find that these correlations are strong in different
real multiplexes, and form a key framework for answering many important
questions. Specifically, we show that these geometric correlations facilitate:
(i) the definition and detection of multidimensional communities, which are
sets of nodes that are simultaneously similar in multiple layers; (ii) accurate
trans-layer link prediction, where connections in one layer can be predicted by
observing the hidden geometric space of another layer; and (iii) efficient
targeted navigation in the multilayer system using only local knowledge, which
outperforms navigation in the single layers only if the geometric correlations
are sufficiently strong. Our findings uncover fundamental organizing principles
behind real multiplexes and can have important applications in diverse domains.Comment: Supplementary Materials available at
http://www.nature.com/nphys/journal/v12/n11/extref/nphys3812-s1.pd
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