73 research outputs found
Update rules and interevent time distributions: Slow ordering vs. no ordering in the Voter Model
We introduce a general methodology of update rules accounting for arbitrary
interevent time distributions in simulations of interacting agents. In
particular we consider update rules that depend on the state of the agent, so
that the update becomes part of the dynamical model. As an illustration we
consider the voter model in fully-connected, random and scale free networks
with an update probability inversely proportional to the persistence, that is,
the time since the last event. We find that in the thermodynamic limit, at
variance with standard updates, the system orders slowly. The approach to the
absorbing state is characterized by a power law decay of the density of
interfaces, observing that the mean time to reach the absorbing state might be
not well defined.Comment: 5pages, 4 figure
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
Heterogeneity shapes groups growth in social online communities
Many complex systems are characterized by broad distributions capturing, for
example, the size of firms, the population of cities or the degree distribution
of complex networks. Typically this feature is explained by means of a
preferential growth mechanism. Although heterogeneity is expected to play a
role in the evolution it is usually not considered in the modeling probably due
to a lack of empirical evidence on how it is distributed. We characterize the
intrinsic heterogeneity of groups in an online community and then show that
together with a simple linear growth and an inhomogeneous birth rate it
explains the broad distribution of group members.Comment: 5 pages, 3 figure panel
Quasiperiodic Patterns in Boundary-Modulated Excitable Waves
We investigate the impact of the domain shape on wave propagation in
excitable media. Channelled domains with sinusoidal boundaries are considered.
Trains of fronts generated periodically at an extreme of the channel are found
to adopt a quasiperiodic spatial configuration stroboscopically frozen in time.
The phenomenon is studied in a model for the photo-sensitive
Belousov-Zabotinsky reaction, but we give a theoretical derivation of the
spatial return maps prescribing the height and position of the successive
fronts that is valid for arbitrary excitable reaction-diffusion systems.Comment: 4 pages (figures included
Anomalous lifetime distributions and topological traps in ordering dynamics
We address the role of community structure of an interaction network in
ordering dynamics, as well as associated forms of metastability. We consider
the voter and AB model dynamics in a network model which mimics social
interactions. The AB model includes an intermediate state between the two
excluding options of the voter model. For the voter model we find dynamical
metastable disordered states with a characteristic mean lifetime. However, for
the AB dynamics we find a power law distribution of the lifetime of metastable
states, so that the mean lifetime is not representative of the dynamics. These
trapped metastable states, which can order at all time scales, originate in the
mesoscopic network structure.Comment: 7 pages; 6 figure
Perturbation: the Catastrophe Causer in Scale-Free Networks
A new model about cascading occurrences caused by perturbation is established
to search after the mechanism because of which catastrophes in networks occur.
We investigate the avalanche dynamics of our model on 2-dimension Euclidean
lattices and scale-free networks and find out the avalanche dynamic behaviors
is very sensitive to the topological structure of networks. The experiments
show that the catastrophes occur much more frequently in scale-free networks
than in Euclidean lattices and the greatest catastrophe in scale-free networks
is much more serious than that in Euclidean lattices. Further more, we have
studied how to reduce the catastrophes' degree, and have schemed out an
effective strategy, called targeted safeguard-strategy for scale-free networks.Comment: 4 pages, 6 eps figure
The Epidemics of Donations: Logistic Growth and Power-Laws
This paper demonstrates that collective social dynamics resulting from individual donations can be well described by an epidemic model. It captures the herding behavior in donations as a non-local interaction between individual via a time-dependent mean field representing the mass media. Our study is based on the statistical analysis of a unique dataset obtained before and after the tsunami disaster of 2004. We find a power-law behavior for the distributions of donations with similar exponents for different countries. Even more remarkably, we show that these exponents are the same before and after the tsunami, which accounts for some kind of universal behavior in donations independent of the actual event. We further show that the time-dependent change of both the number and the total amount of donations after the tsunami follows a logistic growth equation. As a new element, a time-dependent scaling factor appears in this equation which accounts for the growing lack of public interest after the disaster. The results of the model are underpinned by the data analysis and thus also allow for a quantification of the media influence
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
- âŚ