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.

A dynamic approach merging network theory and credit risk techniques to assess systemic risk in financial networks.

Abstract: The interconnectedness of financial institutions affects instability and credit crises. To quantify systemic risk we introduce here the PD model, a dynamic model that combines credit risk techniques with a contagion mechanism on the network of exposures among banks. A potential loss distribution is obtained through a multi-period Monte Carlo simulation that considers the probability of default (PD) of the banks and their tendency of defaulting in the same time interval. A contagion process increases the PD of banks exposed toward distressed counterparties. The systemic risk is measured by statistics of the loss distribution, while the contribution of each node is quantified by the new measures PDRank and PDImpact. We illustrate how the model works on the network of the European Global Systemically Important Banks. For a certain range of the banks’ capital and of their assets volatility, our results reveal the emergence of a strong contagion regime where lower default correlation between banks corresponds to higher losses. This is the opposite of the diversification benefits postulated by standard credit risk models used by banks and regulators who could therefore underestimate the capital needed to overcome a period of crisis, thereby contributing to the financial system instability.

An active-radio-frequency-identification system capable of identifying co-locations and social-structure: Validation with a wild free-ranging animal

Abstract: Behavioural events that are important for understanding sociobiology and movement ecology are often rare, transient and localised, but can occur at spatially distant sites e.g. territorial incursions and co-locating individuals. Existing animal tracking technologies, capable of detecting such events, are limited by one or more of: battery life; data resolution; location accuracy; data security; ability to co-locate individuals both spatially and temporally. Technology that at least partly resolves these limitations would be advantageous. European badgers (Meles meles L.), present a challenging test-bed, with extra-group paternity (apparent from genotyping) contradicting established views on rigid group territoriality with little social-group mixing. In a proof of concept study we assess the utility of a fully automated active-radio-frequency-identification (aRFID) system combining badger-borne aRFID-tags with static, wirelessly-networked, aRFID-detector base-stations to record badger co-locations at setts (burrows) and near notional border latrines. We summarise the time badgers spent co-locating within and between social-groups, applying network analysis to provide evidence of co-location based community structure, at both these scales. The aRFID system co-located animals within 31.5 m (adjustable) of base-stations. Efficient radio transmission between aRFIDs and base-stations enables a 20 g tag to last for 2–5 years (depending on transmission interval). Data security was high (data stored off tag), with remote access capability. Badgers spent most co-location time with members of their own social-groups at setts; remaining co-location time was divided evenly between intra- and inter-social-group co-locations near latrines and inter-social-group co-locations at setts. Network analysis showed that 20–100% of tracked badgers engaged in inter-social-group mixing per week, with evidence of trans-border super-groups, that is, badgers frequently transgressed notional territorial borders. aRFID occupies a distinct niche amongst established tracking technologies. We validated the utility of aRFID to identify co-locations, social-structure and inter-group mixing within a wild badger population, leading us to refute the conventional view that badgers (social-groups) are territorial and to question management strategies, for controlling bovine TB, based on this model. Ultimately aRFID proved a versatile system capable of identifying social-structure at the landscape scale, operating for years and suitable for use with a range of species.

Nonparametric resampling of random walks for spectral network clustering.

Abstract: Parametric resampling schemes have been recently introduced in complex network analysis with the aim of assessing the statistical significance of graph clustering and the robustness of community partitions. We propose here a method to replicate structural features of complex networks based on the non-parametric resampling of the transition matrix associated with an unbiased random walk on the graph. We test this bootstrapping technique on synthetic and real-world modular networks and we show that the ensemble of replicates obtained through resampling can be used to improve the performance of standard spectral algorithms for community detection.

On Nonstationarity of Human Contact Networks

Abstract: The measurement and the analysis of the temporal patterns arising in human networks is of fundamental importance to many application domains including targeted advertising, opportunistic routing, resource provisioning (e.g., bandwidth allocation in infrastructured wireless networks) and, more in general, modeling of human social behavior. In this paper we present a novel and exhaustive study of the temporal dynamics of human networks and apply it to different sets of wireless network traces. We consider networks of contacts among users (i.e., peer-to-peer opportunistic networks). We show that we are able to quantify how the amount of information associated to the process evolves over time by using techniques based on time series analysis. We also demonstrate how regular patterns appear only at certain time scales: network dynamics appears nonstationary, in the sense that its statistical description is different at various time scales. These results provide a new methodology to accurately and quantitatively investigate the temporal properties of any type of human interactions and open new directions towards a better understanding of the regular nature of human social behavior.

Inhibiting failure spreading in complex networks

Abstract: In our daily lives, we rely on the proper functioning of supply networks, from power grids to water transmission systems. A single failure in these critical infrastructures can lead to a complete collapse through a cascading failure mechanism. Counteracting strategies are thus heavily sought after. In this article, we introduce a general framework to analyse the spreading of failures in complex networks and demonstrate that both weak and strong connections can be used to contain damages. We rigorously prove the existence of certain subgraphs, called network isolators, that can completely inhibit any failure spreading, and we show how to create such isolators in synthetic and real-world networks. The addition of selected links can thus prevent large scale outages as demonstrated for power transmission grids.

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.