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Vito Latora

Researcher at Queen Mary University of London

Publications -  360
Citations -  41121

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.

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Street centrality vs. commerce and service locations in cities: a Kernel Density Correlation case study in Bologna, Italy

TL;DR: In this paper, the authors investigated the case of Bologna, northern Italy, about how much higher street centrality statistically "determines" a higher presence of activities (shops and services).
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The evolution of knowledge within and across fields in modern physics

TL;DR: A quantitative framework is developed to extract significant dependencies among scientific disciplines and turn them into a time-varying network whose nodes are the different fields, while the weighted links represent the flow of knowledge from one field to another at a given period of time.
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Searching for the nuclear liquid-gas phase transition in Au+Au collisions at 35 MeV/nucleon.

TL;DR: It is found that the collision Au+Au at an incident energy of 35 MeV/nucleon shows a critical behavior at peripheral impact parameters, revealed through the analysis of conditional moments of charge distributions, Campi scatter plot, and the occurrence of large fluctuations in the region of the Campi plot where this critical behavior is expected.
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The rate of entropy increase at the edge of chaos

TL;DR: In this paper, it was shown that the Boltzmann-gibbs-Shannon entropy is not appropriate for the edge-of-chaos case, and instead, the non-extensive entropy (S_q\equiv \frac{1-\sum{i=1}^W p_i^q}{q-1}$ must be used.
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Quantifying and predicting success in show business.

TL;DR: In this paper, the authors study the careers of actors and identify a "rich-get-richer" mechanism with respect to productivity, the emergence of hot streaks and the presence of gender bias, and are able to predict whether the most productive year of an actor is yet to come.