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.

Evolutionary Dynamics of Higher-Order Interactions.

Abstract: We live and cooperate in networks. However, links in networks only allow for pairwise interactions, thus making the framework suitable for dyadic games, but not for games that are played in groups of more than two players. Here, we study the evolutionary dynamics of a public goods game in social systems with higher-order interactions. First, we show that the game on uniform hypergraphs corresponds to the replicator dynamics in the well-mixed limit, providing a formal theoretical foundation to study cooperation in networked groups. Secondly, we unveil how the presence of hubs and the coexistence of interactions in groups of different sizes affects the evolution of cooperation. Finally, we apply the proposed framework to extract the actual dependence of the synergy factor on the size of a group from real-world collaboration data in science and technology. Our work provides a way to implement informed actions to boost cooperation in social groups.

Effects of traffic properties and degree heterogeneity in flow fluctuations on complex networks

Abstract: Communication networks are nowadays crucial in our lives and the study of the traffic features yields important advantages. In both network and traffic design, the understanding of the relationship between the traffic on a node and its fluctuations plays a key role. In this paper, we investigate the relationship between the mean traffic flow experienced by a node and its standard deviation via numerical simulations and real data analysis. In particular, we show the great influence that the degree heterogeneity of real communication systems has on the patterns of flow fluctuations observed across complex communication networks. To this end, we derive an analytical law connecting the standard deviation of flows and their mean values, we prove it via extensive numerical simulations and by means of a realistic internet traffic simulator software: NS-3. We also show that our results are robust under different assumptions regarding network topology, routing strategy and packets injection distributions.

Modules identification by a Dynamical Clustering algorithm based on chaotic Rössler oscillators

Abstract: A new dynamical clustering algorithm for the identification of modules in complex networks has been recently introduced [1]. In this paper we present a modified version of this algorithm based on a system of chaotic Rossler oscillators and we test its sensitivity on real and computer generated networks with a well known modular structure.

High-resolution maps of hunter-gatherer social networks reveal human adaptation for cultural exchange

Abstract: Are interactions with unrelated and even unknown individuals a by-product of modern life in megacities? Here we argue instead that social ties among non-kin are a crucial human adaptation. By deploying a new portable wireless sensing technology (motes), we mapped social networks in Agta and BaYaka hunter-gatherers in unprecedented detail. We show that strong friendships with non-kin optimize the global efficiency of their social networks thereby facilitating cultural exchange, and that the adaptation for forming friendship ties appears early in development. The ability to extend networks and form strong non-kin ties may explain some human distinctive characteristics such as hypersociality and cumulative culture, and the tendency to exchange ideas with unrelated and unknown individuals in megacities and online social networks.

Scaling and universality in river flow dynamics

Abstract: We 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. Copyright c EPLA, 2011

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.