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