184 research outputs found
Explosive synchronization enhanced by time-delayed coupling
We study the emergence of synchronization in scale-free networks by
considering the Kuramoto model of coupled phase oscillators. The natural
frequencies of oscillators are assumed to be correlated with their degrees and
a time delay is included in the system. This assumption allows enhancing the
explosive transition to reach the synchronous state. We provide an analytical
treatment developed in a star graph which reproduces results obtained in
scale-free networks. Our findings have important implications in understanding
the synchronization of complex networks, since the time delay is present in
most systems due to the finite speed of the signal transmission over a
distance.Comment: 5 pages, 7 figure
Modeling Worldwide Highway Networks
This letter addresses the problem of modeling the highway systems of
different countries by using complex networks formalism. More specifically, we
compare two traditional geographical models with a modified geometrical network
model where paths, rather than edges, are incorporated at each step between the
origin and destination nodes. Optimal configurations of parameters are obtained
for each model and used in the comparison. The highway networks of Brazil, the
US and England are considered and shown to be properly modeled by the modified
geographical model. The Brazilian highway network yielded small deviations that
are potentially accountable by specific developing and sociogeographic features
of that country.Comment: 5 pages, 3 figures, 1 tabl
Collective behavior in financial market
Financial market is an example of complex system, which is characterized by a
highly intricate organization and the emergence of collective behavior. In this
paper, we quantify this emergent dynamics in the financial market by using
concepts of network synchronization. We consider networks constructed by the
correlation matrix of asset returns and study the time evolution of the phase
coherence among stock prices. It is verified that during financial crisis a
synchronous state emerges in the system, defining the market's direction.
Furthermore, the paper proposes a statistical regression model able to identify
the topological features that mostly influence such an emergence. The
coefficients of the proposed model indicate that the average shortest path
length is the measurement most related to network synchronization. Therefore,
during economic crisis, the stock prices present a similar evolution, which
tends to shorten the distances between stocks, indication a collective
dynamics.Comment: 5 pages, 2 figure
Signal Propagation in Cortical Networks: A Digital Signal Processing Approach
This work reports a digital signal processing approach to representing and modeling transmission and combination of signals in cortical networks. The signal dynamics is modeled in terms of diffusion, which allows the information processing undergone between any pair of nodes to be fully characterized in terms of a finite impulse response (FIR) filter. Diffusion without and with time decay are investigated. All filters underlying the cat and macaque cortical organization are found to be of low-pass nature, allowing the cortical signal processing to be summarized in terms of the respective cutoff frequencies (a high cutoff frequency meaning little alteration of signals through their intermixing). Several findings are reported and discussed, including the fact that the incorporation of temporal activity decay tends to provide more diversified cutoff frequencies. Different filtering intensity is observed for each community in those networks. In addition, the brain regions involved in object recognition tend to present the highest cutoff frequencies for both the cat and macaque networks
Modeling the effects of social distancing on the large-scale spreading of diseases
To contain the propagation of emerging diseases that are transmissible from human to human, non-pharmaceutical interventions (NPIs) aimed at reducing the interactions between humans are usually implemented. One example of the latter kind of measures is social distancing, which can be either policy-driven or can arise endogenously in the population as a consequence of the fear of infection. However, if NPIs are lifted before the population reaches herd immunity, further re-introductions of the pathogen would lead to secondary infections. Here we study the effects of different social distancing schemes on the large scale spreading of diseases. Specifically, we generalize metapopulation models to include social distancing mechanisms at the subpopulation level and model short- and long-term strategies that are fed with local or global information about the epidemics. We show that different model ingredients might lead to very diverse outcomes in different subpopulations. Our results suggest that there is not a unique answer to the question of whether contention measures are more efficient if implemented and managed locally or globally and that model outcomes depends on how the full complexity of human interactions is taken into account
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