993 research outputs found
Networking - A Statistical Physics Perspective
Efficient networking has a substantial economic and societal impact in a
broad range of areas including transportation systems, wired and wireless
communications and a range of Internet applications. As transportation and
communication networks become increasingly more complex, the ever increasing
demand for congestion control, higher traffic capacity, quality of service,
robustness and reduced energy consumption require new tools and methods to meet
these conflicting requirements. The new methodology should serve for gaining
better understanding of the properties of networking systems at the macroscopic
level, as well as for the development of new principled optimization and
management algorithms at the microscopic level. Methods of statistical physics
seem best placed to provide new approaches as they have been developed
specifically to deal with non-linear large scale systems. This paper aims at
presenting an overview of tools and methods that have been developed within the
statistical physics community and that can be readily applied to address the
emerging problems in networking. These include diffusion processes, methods
from disordered systems and polymer physics, probabilistic inference, which
have direct relevance to network routing, file and frequency distribution, the
exploration of network structures and vulnerability, and various other
practical networking applications.Comment: (Review article) 71 pages, 14 figure
Traffic-driven Epidemic Spreading in Finite-size Scale-Free Networks
The study of complex networks sheds light on the relation between the
structure and function of complex systems. One remarkable result is the absence
of an epidemic threshold in infinite-size scale-free networks, which implies
that any infection will perpetually propagate regardless of the spreading rate.
The vast majority of current theoretical approaches assumes that infections are
transmitted as a reaction process from nodes to all neighbors. Here we adopt a
different perspective and show that the epidemic incidence is shaped by traffic
flow conditions. Specifically, we consider the scenario in which epidemic
pathways are defined and driven by flows. Through extensive numerical
simulations and theoretical predictions, it is shown that the value of the
epidemic threshold in scale-free networks depends directly on flow conditions,
in particular on the first and second moments of the betweenness distribution
given a routing protocol. We consider the scenarios in which the delivery
capability of the nodes is bounded or unbounded. In both cases, the threshold
values depend on the traffic and decrease as flow increases. Bounded delivery
provokes the emergence of congestion, slowing down the spreading of the disease
and setting a limit for the epidemic incidence. Our results provide a general
conceptual framework to understand spreading processes on complex networks.Comment: Final version to be published in Proceedings of the National Academy
of Sciences US
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
Path-Based Epidemic Spreading in Networks
Conventional epidemic models assume omnidirectional contact-based infection. This strongly associates the epidemic spreading process with node degrees. The role of the infection transmission medium is often neglected. In real-world networks, however, the infectious agent as the physical contagion medium usually flows from one node to another via specific directed routes ( path-based infection). Here, we use continuous-time Markov chain analysis to model the influence of the infectious agent and routing paths on the spreading behavior by taking into account the state transitions of each node individually, rather than the mean aggregated behavior of all nodes. By applying a mean field approximation, the analysis complexity of the path-based infection mechanics is reduced from exponential to polynomial. We show that the structure of the topology plays a secondary role in determining the size of the epidemic. Instead, it is the routing algorithm and traffic intensity that determine the survivability and the steady-state of the epidemic. We define an infection characterization matrix that encodes both the routing and the traffic information. Based on this, we derive the critical path-based epidemic threshold below which the epidemic will die off, as well as conditional bounds of this threshold which network operators may use to promote/suppress path-based spreading in their networks. Finally, besides artificially generated random and scale-free graphs, we also use real-world networks and traffic, as case studies, in order to compare the behaviors of contact- and path-based epidemics. Our results further corroborate the recent empirical observations that epidemics in communication networks are highly persistent
Urban characteristics attributable to density-driven tie formation
Motivated by empirical evidence on the interplay between geography,
population density and societal interaction, we propose a generative process
for the evolution of social structure in cities. Our analytical and simulation
results predict both super-linear scaling of social tie density and information
flow as a function of the population. We demonstrate that our model provides a
robust and accurate fit for the dependency of city characteristics with city
size, ranging from individual-level dyadic interactions (number of
acquaintances, volume of communication) to population-level variables
(contagious disease rates, patenting activity, economic productivity and crime)
without the need to appeal to modularity, specialization, or hierarchy.Comment: Early version of this paper was presented in NetSci 2012 as a
contributed talk in June 2012. An improved version of this paper is published
in Nature Communications in June 2013. It has 14 pages and 5 figure
Epidemic processes in complex networks
In recent years the research community has accumulated overwhelming evidence
for the emergence of complex and heterogeneous connectivity patterns in a wide
range of biological and sociotechnical systems. The complex properties of
real-world networks have a profound impact on the behavior of equilibrium and
nonequilibrium phenomena occurring in various systems, and the study of
epidemic spreading is central to our understanding of the unfolding of
dynamical processes in complex networks. The theoretical analysis of epidemic
spreading in heterogeneous networks requires the development of novel
analytical frameworks, and it has produced results of conceptual and practical
relevance. A coherent and comprehensive review of the vast research activity
concerning epidemic processes is presented, detailing the successful
theoretical approaches as well as making their limits and assumptions clear.
Physicists, mathematicians, epidemiologists, computer, and social scientists
share a common interest in studying epidemic spreading and rely on similar
models for the description of the diffusion of pathogens, knowledge, and
innovation. For this reason, while focusing on the main results and the
paradigmatic models in infectious disease modeling, the major results
concerning generalized social contagion processes are also presented. Finally,
the research activity at the forefront in the study of epidemic spreading in
coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio
Superprocesses as models for information dissemination in the Future Internet
Future Internet will be composed by a tremendous number of potentially
interconnected people and devices, offering a variety of services, applications
and communication opportunities. In particular, short-range wireless
communications, which are available on almost all portable devices, will enable
the formation of the largest cloud of interconnected, smart computing devices
mankind has ever dreamed about: the Proximate Internet. In this paper, we
consider superprocesses, more specifically super Brownian motion, as a suitable
mathematical model to analyse a basic problem of information dissemination
arising in the context of Proximate Internet. The proposed model provides a
promising analytical framework to both study theoretical properties related to
the information dissemination process and to devise efficient and reliable
simulation schemes for very large systems
- …