We propose and document the evidence for an analogy between the dynamics of granular counter-flows in the presence of bottlenecks or restrictions and financial price formation processes. Using extensive simulations, we find that the counter-flows of simulated pedestrians through a door display many stylized facts observed in financial markets when the density around the door is compared with the logarithm of the price. The stylized properties are present already when the agents in the pedestrian model are assumed to display a zero-intelligent behavior. If agents are given decision-making capacity and adapt to partially follow the majority, periods of herding behavior may additionally occur. This generates the very slow decay of the autocorrelation of absolute return due to an intermittent dynamics. Our finding suggest that the stylized facts in the fluctuations of the financial prices result from a competition of two groups with opposite interests in the presence of a constraint funneling the flow of transactions to a narrow band of prices.

How far and how fast does information spre 95e ad in social media? Researchers have recently examined a number of factors that affect information diffusion in online social networks, including: the novelty of information, users' activity levels, who they pay attention to, and how they respond to friends' recommendations. Using URLs as markers of information, we carry out a detailed study of retweeting, the primary mechanism by which information spreads on the Twitter follower graph. Our empirical study examines how users respond to an incoming stimulus, i.e., a tweet (message) from a friend, and reveals that %retweeting behavior is constrained by a few simple principles. the "principle of least effort" combined with limited attention plays a dominant role in retweeting behavior. Specifically, we observe that users retweet information when it is most visible, such as when it near the top of their Twitter stream. Moreover, our measurements quantify how a user's limited attention is divided among incoming tweets, providing novel evidence that highly connected individuals are less likely to propagate an arbitrary tweet. Our study indicates that the finite ability to process incoming information constrains social contagion, and we conclude that rapid decay of visibility is the primary barrier to information propagation online.

We 7e4 extend the formalism of Random Boolean Networks with canalizing rules to multilevel complex networks. The formalism allows to model genetic networks in which each gene might take part in more than one signaling pathway. We use a semi-annealed approach to study the stability of this class of models when coupled in a multiplex network and show that the analytical results are in good agreement with numerical simulations. Our main finding is that the multiplex structure provides a mechanism for the stabilization of the system and of chaotic regimes of individual layers. Our results help understanding why some genetic networks that are theoretically expected to operate in the chaotic regime can actually display dynamical stability.

A system of interdependent networks was recently found to be very vulnerable since cascading failures that may lead to abrupt breakdown of the system. We develop an analytical method, based on the percolation method developed for single networks [M.E.J. Newman, Phys. Rev. Lett. {\bf 103}, 058701 (2009)], to study the effect of clustering within the networks on the robustness of the interdependent networks. We find that, in contrast to single networks where the percolation threshold, $p_c$, does not change with clustering for site percolation and {\it decreases} with clustering for bond percolation, $p_c$ for interdependent networks {\it increases} when networks are more clustered.

We present a generalized method for calculating the k-shell structure of weighted networks. The method takes into ac 80b count both the weight and the degree of a network, in such a way that in the absence of weights we resume the shell structure obtained by the classic k-shell decomposition. In the presence of weights we show that the method is able to partition the network in a more refined way, without the need of any arbitrary threshold on the weight values. Furthermore, by simulating spreading processes using the Susceptible-Infectious-Recovered model in four different real weighted networks, we show that the weighted k-shell decomposition method ranks the nodes more accurately, by placing nodes with higher spreading potential into shells closer to the core. In addition we demonstrate our new method on a real economic network and show that the core calculated using the weighted k-shell method is more meaningful from an economics perspective when compared to the unweighted method.