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.

Testing the Mean-Field Approximation in the Spinodal Region

Abstract: The Boltzmann-Nordheim-Vlasov (BNV) equation 1, also known as the Boltz-mann-Uehling-Uhlenbeck equation, has been extensively used in understanding the dynamics of intermediate energy heavy-ion collisions 2. This kinetic equation provides a good basis for describing the average properties of one-body observables in situations where the fluctuations are small and the effects of correlations are not important. However, when one is interested in processes in which the high-order correlations play a dominant role like, for instance, multifragmentation, such models do not provide a realistic description and are not valid anymore.

Dynamics of Multifragmentation

Abstract: We investigate the possibility of occurrence of a liquid-gas phase transition in a finite system. Through a study of mass distributions, scaled factorial moments and moments of cluster mass distributions, we find evidence for the presence of a critical behavior of our finite system. Furthermore, by studying scaling invariance of hydrodynamical equations in the framework of classical molecular dynamics, it is shown that hydrodynamical scaling is valid at high beam energies and not at low beam energies. At the beam energy where the violation of the scaling occurs, one observes a mass distribution exhibiting a power law which corresponds to the occurrence of a phase transition.

Non-Markovian temporal networks with auto- and cross-correlated link dynamics

Abstract: Many of the biological, social and man-made networks around us are inherently dynamic, with their links switching on and off over time. The evolution of these networks is often non-Markovian, and the dynamics of their links correlated. Hence, to accurately model these networks, predict their evolution, and understand how information and other quantities propagate over them, the inclusion of both memory and dynamical dependencies between links is key. We here introduce a general class of models of temporal networks based on discrete autoregressive processes. As a case study we concentrate on a specific model within this class, generating temporal networks with a specified underlying backbone, and with precise control over the dynamical dependencies between links and the strength and length of their memories. In this network model the presence of each link is influenced by its own past activity and the past activities of other links, as specified by a coupling matrix, which directly controls the causal relations and correlations among links. We propose a method for estimating the models parameters and how to deal with heterogeneity and time-varying patterns, showing how the model allows for a more realistic description of real world temporal networks and also to predict their evolution. We then investigate the role that memory and correlations in link dynamics have on processes occurring over a temporal network by studying the speed of a spreading process, as measured by the time it takes for diffusion to reach equilibrium. Through both numerical simulations and analytical results, we are able to separate the roles of autocorrelations and neighbourhood correlations in link dynamics, showing that the speed of diffusion is non-monotonically dependent on the memory length, and that correlations among neighbouring links can speed up the spreading process, while autocorrelations slow it down.

Collapse transition in epidemic spreading subject to detection with limited resources

Abstract: Compartmental models are the most widely used framework for modeling infectious diseases. These models have been continuously refined to incorporate all the realistic mechanisms that can shape the course of an epidemic outbreak. Building on a compartmental model that accounts for early detection and isolation of infectious individuals through testing, in this article we focus on the viability of detection processes under limited availability of testing resources, and we study how the latter impacts on the detection rate. Our results show that, in addition to the well-known epidemic transition at ${\mathcal{R}}_0=1$, a second transition occurs at ${\mathcal{R}}^*_0>1$ pinpointing the collapse of the detection system and, as a consequence, the switch from a regime of mitigation to a regime in which the pathogen spreads freely. We characterize the epidemic phase diagram of the model as a function of the relevant control parameters: the basic reproduction number, the maximum detection capacity of the system, and the fraction of individuals in shelter. Our analysis thus provides a valuable tool for estimating the detection resources and the level of confinement needed to face epidemic outbreaks.

Reconstructing higher-order interactions in coupled dynamical systems

Abstract: Higher-order interactions play a key role for the stability and function of a complex system. However, how to identify them is still an open problem. Here, we propose a method to fully reconstruct the structural connectivity of a system of coupled dynamical units, identifying both pairwise and higher-order interactions from the system time evolution. Our method works for any dynamics, and allows the reconstruction of both hypergraphs and simplicial complexes, either undirected or directed, unweighted or weighted. With two concrete applications, we show how the method can help understanding the ecosystemic complexity of bacterial systems, or the microscopic mechanisms of interaction underlying coupled chaotic oscillators.

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.