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Most complex networks serve as conduits for various dynamical processes, ranging from mass transfer by chemical reactions in the cell to packet transfer on the Internet. We collected data on the time dependent activity of five natural and technological networks, finding that for each the coupling of the flux fluctuations with the total flux on individual nodes obeys a unique scaling law. We show that the observed scaling can explain the competition between the systems internal collective dynamics and changes in the external environment, allowing us to predict the relevant scaling exponents.
The parallel dynamics of the fully connected Blume-Emery-Griffiths neural network model is studied for arbitrary temperature. By employing a probabilistic signal-to-noise approach, a recursive scheme is found determining the time evolution of the dis
An inverse procedure is proposed and tested which aims at recovering the a priori unknown functional and structural information from global signals of living brains activity. To this end we consider a Leaky-Integrate and Fire (LIF) model with short t
An Ashkin-Teller neural network, allowing for two types of neurons is considered in the case of low loading as a function of the strength of the respective couplings between these neurons. The storage and retrieval of embedded patterns built from the
We employ a functional renormalization group to study interfaces in the presence of a pinning potential in $d=4-epsilon$ dimensions. In contrast to a previous approach [D.S. Fisher, Phys. Rev. Lett. {bf 56}, 1964 (1986)] we use a soft-cutoff scheme.
We review three studies of information flow in social networks that help reveal their underlying social structure, how information spreads through them and why small world experiments work.