341 research outputs found
How the science of complex networks can help developing strategies against terrorism
No full textA new method, based on a recently defined centrality measure, allows to spot the critical components of a generic complex network. The identification and protection of the critical components of a given communication–transportation network should be the first concern in order to reduce the consequences of terrorist attacks. On the other hand, the critical components of a terrorist organization are the terrorists to target to disrupt the organization and reduce the possibility of terroristic attacks
A measure of centrality based on network efficiency
No full textWe introduce delta centralities, a new class of measures of structural centrality for networks. In particular, we focus on a measure in this class, the information centrality CI, which is based on the concept of efficient propagation of information over the network. CI is defined for both valued and non-valued graphs, and applies to groups as well as individuals. The measure is illustrated and compared with respect to the standard centrality measures by using a classic network data set. The statistical distribution of information centrality is investigated by considering large computer generated graphs and two networks from the real world
Fast detection of nonlinearity and nonstationarity in short and noisy time series
No full textWe introduce a statistical method to detect nonlinearity and nonstationarity in time series, that works even for short sequences and in the presence of noise. The method has a discrimination power similar to that of the most advanced estimators on the market, yet it depends only on one parameter, is easier to implement and faster. Applications to real data sets reject the null hypothesis of an underlying stationary linear stochastic process with a higher confidence interval than the best known nonlinear discriminators up to date
Scaling and universality in river flow dynamics
No full textWe investigate flow dynamics in rivers characterized by basin areas and daily mean discharge spanning different orders of magnitude. We show that the delayed increments evaluated at time scales ranging from days to months can be opportunely rescaled to the same non-Gaussian probability density function. Such a scaling breaks up above a certain critical horizon, where a behavior typical of thermodynamic systems at the critical point emerges. We finally show that both the scaling behavior and the break-up of the scaling are universal features of river flow dynamics
Is the Boston subway a small-world network?
No full textThe mathematical study of the small-world concept has fostered quite some interest, showing that small-world features can be identified for some abstract classes of networks. However, passing to real complex systems, as for instance transportation networks, shows a number of new problems that make current analysis impossible. In this paper we show how a more refined kind of analysis, relying on transportation efficiency, can in fact be used to overcome such problems, and to give precious insights on the general characteristics of real transportation networks, eventually providing a picture where the small-world comes back as underlying construction principle
Efficient behavior of small-world networks
No full textWe introduce the concept of efficiency of a network as a measure of how efficiently it exchanges information. By using this simple measure, small-world networks are seen as systems that are both globally and locally efficient. This gives a clear physical meaning to the concept of “small world,” and also a precise quantitative analysis of both weighted and unweighted networks. We study neural networks and man-made communication and transportation systems and we show that the underlying general principle of their construction is in fact a small-world principle of high efficiency
The spatial analysis of urban systems: Multiple Centrality Assessment and the dynamics on street networks
No full textHarmony in the small-world
No full textThe small-world phenomenon, popularly known as six degrees of separation, has been mathematically formalized by Watts and Strogatz in a study of the topological properties of a network. Small-world networks are defined in terms of two quantities: they have a high clustering coefficient C like regular lattices and a short characteristic path length L typical of random networks. Physical distances are of fundamental importance in applications to real cases; nevertheless, this basic ingredient is missing in the original formulation. Here, we introduce a new concept, the connectivity length D, that gives harmony to the whole theory. D can be evaluated on a global and on a local scale and plays in turn the role of L and 1/C. Moreover, it can be computed for any metrical network and not only for the topological cases. D has a precise meaning in terms of information propagation and describes in a unified way, both the structural and the dynamical aspects of a network: small-worlds are defined by a small global and local D, i.e., by a high efficiency in propagating information both on a local and global scale. The neural system of the nematode C. elegans, the collaboration graph of film actors, and the oldest US subway system, can now be studied also as metrical networks and are shown to be small-worlds
A topological analysis of the Italian electric power grid
No full textLarge-scale blackouts are an intrinsic drawback of electric power transmission grids. Here we analyze the structural vulnerability of the Italian GRTN power grid by using a model for cascading failures recently proposed in Crucitti et al. (Phys. Rev. E 69 (2004))
- …
