479 research outputs found
Complex Systems: Nonlinerity and Structural Complexity in spatially extended and discrete systems
Esta Tesis doctoral aborda el estudio de sistemas de muchos elementos (sistemas discretos) interactuantes. La fenomenología presente en estos sistemas esta dada por la presencia de dos ingredientes fundamentales: (i) Complejidad dinámica: Las ecuaciones del movimiento que rigen la evolución de los constituyentes son no lineales de manera que raramente podremos encontrar soluciones analíticas. En el espacio de fases de estos sistemas pueden coexistir diferentes tipos de trayectorias dinámicas (multiestabilidad) y su topología puede variar enormemente dependiendo de los parámetros usados en las ecuaciones. La conjunción de dinámica no lineal y sistemas de muchos grados de libertad (como los que aquí se estudian) da lugar a propiedades emergentes como la existencia de soluciones localizadas en el espacio, sincronización, caos espacio-temporal, formación de patrones, etc... (ii) Complejidad estructural: Se refiere a la existencia de un alto grado de aleatoriedad en el patrón de las interacciones entre los componentes. En la mayoría de los sistemas estudiados esta aleatoriedad se presenta de forma que la descripción de la influencia del entorno sobre un único elemento del sistema no puede describirse mediante una aproximación de campo medio. El estudio de estos dos ingredientes en sistemas extendidos se realizará de forma separada (Partes I y II de esta Tesis) y conjunta (Parte III). Si bien en los dos primeros casos la fenomenología introducida por cada fuente de complejidad viene siendo objeto de amplios estudios independientes a lo largo de los últimos a¿nos, la conjunción de ambas da lugar a un campo abierto y enormemente prometedor, donde la interdisciplinariedad concerniente a los campos de aplicación implica un amplio esfuerzo de diversas comunidades científicas. En particular, este es el caso del estudio de la dinámica en sistemas biológicos cuyo análisis es difícil de abordar con técnicas exclusivas de la Bioquímica, la Físic
Information sharing in Quantum Complex Networks
We introduce the use of entanglement entropy as a tool for studying the
amount of information shared between the nodes of quantum complex networks. By
considering the ground state of a network of coupled quantum harmonic
oscillators, we compute the information that each node has on the rest of the
system. We show that the nodes storing the largest amount of information are
not the ones with the highest connectivity, but those with intermediate
connectivity thus breaking down the usual hierarchical picture of classical
networks. We show both numerically and analytically that the mutual information
characterizes the network topology. As a byproduct, our results point out that
the amount of information available for an external node connecting to a
quantum network allows to determine the network topology.Comment: text and title updated, published version [Phys. Rev. A 87, 052312
(2013)
Network bipartivity and the transportation efficiency of European passenger airlines
The analysis of the structural organization of the interaction network of a complex system is central to understand its functioning. Here, we focus on the analysis of the bipartivity of graphs. We first introduce a mathematical approach to quantify bipartivity and show its implementation in general and random graphs. Then, we tackle the analysis of the transportation networks of European airlines from the point of view of their bipartivity and observe significant differences between traditional and low cost carriers. Bipartivity shows also that alliances and major mergers of traditional airlines provide a way to reduce bipartivity which, in its turn, is closely related to an increase of the transportation efficiency
Pulsating-campaigns of human prophylaxis driven by risk perception palliate oscillations of direct contact transmitted diseases
Human behavioral responses play an important role in the impact of disease
outbreaks and yet they are often overlooked in epidemiological models.
Understanding to what extent behavioral changes determine the outcome of
spreading epidemics is essential to design effective intervention policies.
Here we explore, analytically, the interplay between the personal decision to
protect oneself from infection and the spreading of an epidemic. We do so by
coupling a decision game based on the perceived risk of infection with a
Susceptible-Infected-Susceptible model. Interestingly, we find that the simple
decision on whether to protect oneself is enough to modify the course of the
epidemics, by generating sustained steady oscillations in the prevalence. We
deem these oscillations detrimental, and propose two intervention policies
aimed at modifying behavioral patterns to help alleviate them. Surprisingly, we
find that pulsating campaigns, compared to continuous ones, are more effective
in diminishing such oscillations.Comment: 19 pages, 6 figure
Correlation dimension of complex networks
We propose a new measure to characterize the dimension of complex networks based on the ergodic theory of dynamical systems. This measure is derived from the correlation sum of a trajectory generated by a random walker navigating the network, and extends the classical Grassberger-Procaccia algorithm to the context of complex networks. The method is validated with reliable results for both synthetic networks and real-world networks such as the world air-transportation network or urban networks, and provides a computationally fast way for estimating the dimensionality of networks which only relies on the local information provided by the walkers
From Scale-free to Erdos-Renyi Networks
We analyze a model that interpolates between scale-free and Erdos-Renyi
networks. The model introduced generates a one-parameter family of networks and
allows to analyze the role of structural heterogeneity. Analytical calculations
are compared with extensive numerical simulations in order to describe the
transition between these two important classes of networks. Finally, an
application of the proposed model to the study of the percolation transition is
presented.Comment: 8 pages, 6 figure
A framework for epidemic spreading in multiplex networks of metapopulations
We propose a theoretical framework for the study of epidemics in structured
metapopulations, with heterogeneous agents, subjected to recurrent mobility
patterns. We propose to represent the heterogeneity in the composition of the
metapopulations as layers in a multiplex network, where nodes would correspond
to geographical areas and layers account for the mobility patterns of agents of
the same class. We analyze both the classical Susceptible-Infected-Susceptible
and the Susceptible-Infected-Removed epidemic models within this framework, and
compare macroscopic and microscopic indicators of the spreading process with
extensive Monte Carlo simulations. Our results are in excellent agreement with
the simulations. We also derive an exact expression of the epidemic threshold
on this general framework revealing a non-trivial dependence on the mobility
parameter. Finally, we use this new formalism to address the spread of diseases
in real cities, specifically in the city of Medellin, Colombia, whose
population is divided into six socio-economic classes, each one identified with
a layer in this multiplex formalism.Comment: 13 pages, 11 figure
- …