Vito Latora

Bio: Vito Latora is an academic researcher from Queen Mary University of London. The author has contributed to research in topics: Complex network & Centrality. The author has an hindex of 78, co-authored 332 publications receiving 35697 citations. Previous affiliations of Vito Latora include University of Catania & University of Paris.

Co-evolution of networks and quantum dynamics: a generalization of preferential attachment

Abstract: We propose a model of network growth in which the network is co-evolving together with the dynamics of a quantum mechanical system, namely a quantum walk taking place over the network. The model naturally generalizes the Barabasi–Albert model of preferential attachment and it has a rich set of tunable parameters, such as the initial conditions of the dynamics or the interaction of the system with its environment. We show that the model produces networks with two-modal power-law degree distributions, super-hubs, finite clustering coefficient, small-world behaviour and non-trivial degree–degree correlations.

Analyse du réseau des voiries urbaines: une approche directe

Abstract: « Entre lesquelles l’une des premières fut que je m’avisai de considérer que souvent il n’y a pas tant de perfection dans les ouvrages composés de plusieurs pièces, et faits de la main de divers maîtres, qu’en ceux auxquels un seul a travaillé. Ainsi voit-on que les bâtiments qu’un seul architecte a entrepris et achevés ont coutume d’être plus beaux et mieux ordonnés que ceux que plusieurs ont tâché de raccommoder, en faisant servir de vieilles murailles qui avoient été bâties à d’autres fins. Ainsi ces anciennes cités qui, n’ayant été au commencement que des bourgades, sont devenues par succession de temps de grandes villes, sont ordinairement si mal compassées, au prix de ces places régulières qu’un ingénieur trace a sa fantaisie dans une plaine, qu’encore que, considérant leurs édifices chacun à part, on y trouve souvent autant ou plus d’art qu’en ceux des autres, toutefois, à voir comme ils sont arrangés, ici un grand, là un petit, et comme ils rendent les rues courbées et inégales, on diroit que c’est plutôt la fortune que la volonté de quelques hommes usants de raison, qui les a ainsi disposés.» (Descartes, Discours de la méthode). À l’aube de la modernité, Descartes définit la géométrie euclidienne comme le seul système ordonné capable, aux yeux d’un homme raisonnable, de s’appliquer à des motifs environnementaux, comme les jardins, les paysages, les routes et les villes. Presque trois cents ans après, Charles-Edouard Jeanneret-Gris dit Le Corbusier réagit violemment contre le retour, prêché par Sitte, à l’esthétique sociale des entrelacs médiévaux, par cette célèbre invective : « La rue courbe est le chemin des ânes, la rue droite le chemin des Hommes » (1925). Seuls des ânes auraient pu concevoir les cités historiques d’Europe, avec leur fouillis de ruelles étroites et cet horrible, chaotique enchevêtrement d’intersections et de places. Actuellement encore, la géométrie euclidienne exerce une sorte de tyrannie sur les architectes et les aménageurs urbains, une obligation lorsqu’il s’agit de tracer des routes, des quartiers ou des villes. Tout comme lors du renouvellement urbain de naguère, les vieux quartiers restent sousestimés quant à leurs valeurs les plus fondamentales : certes ils sont pittoresques, agréables et intéressants en raison de leurs architectures originales, mais leur structure est dévalorisée : ils sont désordonnés.

City ecosystem resilience analysis in case of disasters

Abstract: One of the most important tasks of urban and hazard planning is to mitigate the damages and minimize the costs of the recovery process after catastrophic events. The rapidity and the efficiency of the recovery process are commonly referred to as resilience. Despite the problem of resilience quantification has received a lot of attention, a mathematical definition of the resilience of an urban community, which takes into account the social aspects of a urban environment, has not yet been identified. In this paper we provide and test a methodology for the assessment of urban resilience to catastrophic events which aims at bridging the gap between the engineering and the ecosystem approaches to resilience. We propose to model a urban system by means of different hybrid social-physical complex networks, obtained by enriching the urban street network with additional information about the social and physical constituents of a city, namely citizens, residential buildings and services. Then, we introduce a class of efficiency measures on these hybrid networks, inspired by the definition of global efficiency given in complex network theory, and we show that these measures can be effectively used to quantify the resilience of a urban system, by comparing their respective values before and after a catastrophic event and during the reconstruction process. As a case study, we consider simulated earthquakes in the city of Acerra, Italy, and we use these efficiency measures to compare the ability of different reconstruction strategies in restoring the original performance of the urban system.

The multiplex dependency structure of financial markets

Abstract: We propose here a multiplex network approach to investigate simultaneously different types of dependency in complex data sets. In particular, we consider multiplex networks made of four layers corresponding respectively to linear, non-linear, tail, and partial correlations among a set of financial time series. We construct the sparse graph on each layer using a standard network filtering procedure, and we then analyse the structural properties of the obtained multiplex networks. The study of the time evolution of the multiplex constructed from financial data uncovers important changes in intrinsically multiplex properties of the network, and such changes are associated with periods of financial stress. We observe that some features are unique to the multiplex structure and would not be visible otherwise by the separate analysis of the single-layer networks corresponding to each dependency measure.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

The Structure and Function of Complex Networks

Abstract: Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

Finding and evaluating community structure in networks.

Abstract: We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems.