Vito Latora

TL;DR: In this article, the authors study coauthorship networks based on the preprints submitted to the Los Alamos cond-mat database during the period 2000-2005 and find that the graphs are characterized by long-tailed degree and betweenness distributions, assortative degree-degree correlations, and a power-law dependence of the clustering coefficient on the node degree.

TL;DR: A model in which agents with interrelated opinions, interacting on several layers representing different topics, tend to spread their own ideas to their neighborhood, strive to maintain internal coherence, and is demonstrated how mass media can be effectively used to favor the propagation of a chosen set of opinions, thus polarizing the consensus of an entire population.

TL;DR: The largest Lyapunov exponents $\lambda$ are always positive and can be very well fitted near this "critical temperature" with a functional form $\lambda\propto |T-T_c|^{-\omega}$, where the exponent $\omega=0.15$ is independent of the system and mass number.

TL;DR: In this paper, it was shown that Probability Density Functions (PDFs) for the avalanche size differences at different times have fat tails with a q-Gaussian shape, and that this behavior does not depend on the time interval adopted and it is also found when considering energy differences between real earthquakes.

TL;DR: This work has learned that the network structure can be as important as the nonlinear interactions between elements, and an accurate description of the coupling architecture and a characterization of the structural properties of the network can be of fundamental importance also to understand the dynamics of the system.