45 research outputs found
Shock structures in time averaged patterns for the Kuramoto-Sivashinsky equation
The Kuramoto-Sivashinsky equation with fixed boundary conditions is
numerically studied. Shocklike structures appear in the time-averaged patterns
for some parameter range of the boundary values. Effective diffusion constant
is estimated from the relation of the width and the height of the shock
structures.Comment: 6 pages, 7 figure
Modelling diffusion of innovations in a social network
A new simple model of diffusion of innovations in a social network with
upgrading costs is introduced. Agents are characterized by a single real
variable, their technological level. According to local information agents
decide whether to upgrade their level or not balancing their possible benefit
with the upgrading cost. A critical point where technological avalanches
display a power-law behavior is also found. This critical point is
characterized by a macroscopic observable that turns out to optimize
technological growth in the stationary state. Analytical results supporting our
findings are found for the globally coupled case.Comment: 4 pages, 5 figures. Final version accepted in PR
Effective dimensions and percolation in hierarchically structured scale-free networks
We introduce appropriate definitions of dimensions in order to characterize
the fractal properties of complex networks. We compute these dimensions in a
hierarchically structured network of particular interest. In spite of the
nontrivial character of this network that displays scale-free connectivity
among other features, it turns out to be approximately one-dimensional. The
dimensional characterization is in agreement with the results on statistics of
site percolation and other dynamical processes implemented on such a network.Comment: 5 pages, 5 figure
Nonequilibrium transitions in complex networks: a model of social interaction
We analyze the non-equilibrium order-disorder transition of Axelrod's model
of social interaction in several complex networks. In a small world network, we
find a transition between an ordered homogeneous state and a disordered state.
The transition point is shifted by the degree of spatial disorder of the
underlying network, the network disorder favoring ordered configurations. In
random scale-free networks the transition is only observed for finite size
systems, showing system size scaling, while in the thermodynamic limit only
ordered configurations are always obtained. Thus in the thermodynamic limit the
transition disappears. However, in structured scale-free networks, the phase
transition between an ordered and a disordered phase is restored.Comment: 7 pages revtex4, 10 figures, related material at
http://www.imedea.uib.es/PhysDept/Nonlinear/research_topics/Social
Structural transitions in scale-free networks
Real growing networks like the WWW or personal connection based networks are
characterized by a high degree of clustering, in addition to the small-world
property and the absence of a characteristic scale. Appropriate modifications
of the (Barabasi-Albert) preferential attachment network growth capture all
these aspects. We present a scaling theory to describe the behavior of the
generalized models and the mean field rate equation for the problem. This is
solved for a specific case with the result C(k) ~ 1/k for the clustering of a
node of degree k. Numerical results agree with such a mean-field exponent which
also reproduces the clustering of many real networks.Comment: 4 pages, 3 figures, RevTex forma
The role of caretakers in disease dynamics
One of the key challenges in modeling the dynamics of contagion phenomena is
to understand how the structure of social interactions shapes the time course
of a disease. Complex network theory has provided significant advances in this
context. However, awareness of an epidemic in a population typically yields
behavioral changes that correspond to changes in the network structure on which
the disease evolves. This feedback mechanism has not been investigated in
depth. For example, one would intuitively expect susceptible individuals to
avoid other infecteds. However, doctors treating patients or parents tending
sick children may also increase the amount of contact made with an infecteds,
in an effort to speed up recovery but also exposing themselves to higher risks
of infection. We study the role of these caretaker links in an adaptive network
models where individuals react to a disease by increasing or decreasing the
amount of contact they make with infected individuals. We find that pure
avoidance, with only few caretaker links, is the best strategy for curtailing
an SIS disease in networks that possess a large topological variability. In
more homogeneous networks, disease prevalence is decreased for low
concentrations of caretakers whereas a high prevalence emerges if caretaker
concentration passes a well defined critical value.Comment: 8 pages, 9 figure
Correlations in Scale-Free Networks: Tomography and Percolation
We discuss three related models of scale-free networks with the same degree
distribution but different correlation properties. Starting from the
Barabasi-Albert construction based on growth and preferential attachment we
discuss two other networks emerging when randomizing it with respect to links
or nodes. We point out that the Barabasi-Albert model displays dissortative
behavior with respect to the nodes' degrees, while the node-randomized network
shows assortative mixing. These kinds of correlations are visualized by
discussig the shell structure of the networks around their arbitrary node. In
spite of different correlation behavior, all three constructions exhibit
similar percolation properties.Comment: 6 pages, 2 figures; added reference
A standardisation framework for bioâlogging data to advance ecological research and conservation
Bioâlogging data obtained by tagging animals are key to addressing global conservation challenges. However, the many thousands of existing bioâlogging datasets are not easily discoverable, universally comparable, nor readily accessible through existing repositories and across platforms, slowing down ecological research and effective management. A set of universal standards is needed to ensure discoverability, interoperability and effective translation of bioâlogging data into research and management recommendations.
We propose a standardisation framework adhering to existing data principles (FAIR: Findable, Accessible, Interoperable and Reusable; and TRUST: Transparency, Responsibility, User focus, Sustainability and Technology) and involving the use of simple templates to create a data flow from manufacturers and researchers to compliant repositories, where automated procedures should be in place to prepare data availability into four standardised levels: (a) decoded raw data, (b) curated data, (c) interpolated data and (d) gridded data. Our framework allows for integration of simple tabular arrays (e.g. csv files) and creation of sharable and interoperable network Common Data Form (netCDF) files containing all the needed information for accuracyâofâuse, rightful attribution (ensuring data providers keep ownership through the entire process) and data preservation security.
We show the standardisation benefits for all stakeholders involved, and illustrate the application of our framework by focusing on marine animals and by providing examples of the workflow across all data levels, including filled templates and code to process data between levels, as well as templates to prepare netCDF files ready for sharing.
Adoption of our framework will facilitate collection of Essential Ocean Variables (EOVs) in support of the Global Ocean Observing System (GOOS) and interâgovernmental assessments (e.g. the World Ocean Assessment), and will provide a starting point for broader efforts to establish interoperable bioâlogging data formats across all fields in animal ecology
Relating the microscopic rules in coalescence-fragmentation models to the macroscopic cluster size distributions which emerge
Coalescence-fragmentation problems are of great interest across the physical,
biological, and recently social sciences. They are typically studied from the
perspective of the rate equations, at the heart of such models are the rules
used for coalescence and fragmentation. Here we discuss how changes in these
microscopic rules affect the macroscopic cluster-size distribution which
emerges from the solution to the rate equation. More generally, our work
elucidates the crucial role that the fragmentation rule can play in such
dynamical grouping models. We focus on two well-known models whose
fragmentation rules lie at opposite extremes setting the models within the
broader context of binary coalescence-fragmentation models. Further, we provide
a range of generalizations and new analytic results for a well-known model of
social group formation [V. M. Eguiluz and M. G. Zimmermann, Phys. Rev. Lett.
85, 5659 (2000)]. We develop analytic perturbation treatment of the original
model, and extend the mathematical to the treatment of growing and declining
populations