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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.


Papers
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TL;DR: In this article , the authors propose a general implementation for collective games in which higher-order interactions are encoded on hypergraphs, and employ it for the study of the public goods game by validating the analytical expression of the replicator dynamics in uniform and heterogeneous populations, and then introducing a procedure for retrieving empirical synergistic effects of group interactions from real datasets.
Abstract: Human activities often require simultaneous decision-making of individuals in groups. These processes cannot be coherently addressed by means of networks, as networks only allow for pairwise interactions. Here, we propose a general implementation for collective games in which higher-order interactions are encoded on hypergraphs. We employ it for the study of the public goods game by first validating the analytical expression of the replicator dynamics in uniform and heterogeneous populations, and then by introducing a procedure for retrieving empirical synergistic effects of group interactions from real datasets. U. Alvarez-Rodriguez (B) Data Analytics Group, University of Zurich, Zurich, Switzerland F. Battiston Department of Network and Data Science, Central European University, Vienna, Austria Department of Anthropology, University of Zurich, Zurich, Switzerland G. Ferraz de Arruda · Y. Moreno ISI Foundation, Turin, Italy Y. Moreno Institute for Biocomputation and Physics of Complex Systems, University of Zaragoza, Zaragoza 50008, Spain Department of Theoretical Physics, University of Zaragoza, Zaragoza 50009, Spain M. Perc Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, 2000 Maribor, Slovenia Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan Complexity Science Hub Vienna, Josefstädterstraße 39, 1080 Vienna, Austria V. Latora School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, UK Dipartimento di Fisica ed Astronomia, Università di Catania and INFN, 95123 Catania, Italy The Alan Turing Institute, The British Library, London NW1 2DB, UK © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 F. Battiston and G. Petri (eds.), Higher-Order Systems, Understanding Complex Systems, https://doi.org/10.1007/978-3-030-91374-8_15 377 378 U. Alvarez-Rodriguez et al.

1 citations

Journal ArticleDOI
TL;DR: In this article, the authors investigate the role of social-ties dynamics for the emergence of cooperation in a family of social dilemmas, and they show that the temporal dynamics of social ties has a dramatic impact on the evolution of cooperation: the dynamics of pairwise interactions favors selfish behavior.
Abstract: Cooperation among unrelated individuals is frequently observed in social groups when their members combine efforts and resources to obtain a shared benefit that is unachievable by an individual alone. However, understanding why cooperation arises despite the natural tendency of individuals towards selfish behavior is still an open problem and represents one of the most fascinating challenges in evolutionary dynamics.Recently, the structural characterization of the networks in which social interactions take place has shed some light on the mechanisms by which cooperative behavior emerges and eventually overcomes the natural temptation to defect. In particular, it has been found that the heterogeneity in the number of social ties and the presence of tightly knit communities lead to a significant increase in cooperation as compared with the unstructured and homogeneous connection patterns considered in classical evolutionary dynamics. Here, we investigate the role of social-ties dynamics for the emergence of cooperation in a family of social dilemmas. Social interactions are in fact intrinsically dynamic, fluctuating, and intermittent over time, and they can be represented by time-varying networks. By considering two experimental data sets of human interactions with detailed time information, we show that the temporal dynamics of social ties has a dramatic impact on the evolution of cooperation: the dynamics of pairwise interactions favors selfish behavior.

1 citations

07 Jul 2023
TL;DR: In this paper , the authors introduce a way to quantify the overlap among the hyperedges of a higher-order network, and show that real-world systems exhibit different levels of hyperedge overlap.
Abstract: Recent studies have shown that novel collective behaviors emerge in complex systems due to the presence of higher-order interactions. However, how the collective behavior of a system is influenced by the microscopic organization of its higher-order interactions remains still unexplored. In this Letter, we introduce a way to quantify the overlap among the hyperedges of a higher-order network, and we show that real-world systems exhibit different levels of hyperedge overlap. We then study models of complex contagion and synchronization of phase oscillators, finding that hyperedge overlap plays a universal role in determining the collective dynamics of very different systems. Our results demostrate that the presence of higher-order interactions alone does not guarantee abrupt transitions. Rather, explosivity and bistability require a microscopic organization of the structure with a low value of hyperedge overlap.

1 citations

Posted Content
TL;DR: The results suggest that launching an interdisciplinary career may require more time and persistence to overcome extra challenges, but can pave the way for a more successful endeavour, as well as provide insights on its role in the modern research funding landscape that may be useful to researchers and funding bodies alike.
Abstract: Interdisciplinary research is fundamental when it comes to tackling complex problems in our highly interlinked world, and is on the rise globally. Yet, it is unclear why--in an increasingly competitive academic environment--one should pursue an interdisciplinary career given its recent negative press. Several studies have indeed shown that interdisciplinary research often achieves lower impact compared to more specialized work, and is less likely to attract funding. We seek to reconcile such evidence by analyzing a dataset of 44,419 research grants awarded between 2006 and 2018 from the seven national research councils in the UK. We compared the research performance of researchers with an interdisciplinary funding track record with those who have a specialized profile. We found that the former dominates the network of academic collaborations, both in terms of centrality and knowledge brokerage; but such a competitive advantage does not immediately translate into impact. Indeed, by means of a matched pair experimental design, we found that researchers who transcend between disciplines on average achieve lower impacts in their publications than the subject specialists in the short run, but eventually outperform them in funding performance, both in terms of volume and value. Our results suggest that launching an interdisciplinary career may require more time and persistence to overcome extra challenges, but can pave the way for a more successful endeavour.

1 citations

Proceedings ArticleDOI
01 Sep 2005

1 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
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 …

33,785 citations

28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组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�ly.
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。

18,940 citations

Journal ArticleDOI
TL;DR: Developments in this field are reviewed, 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.
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.

17,647 citations

Journal ArticleDOI
TL;DR: It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.
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.

12,882 citations