V
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.
Papers
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Mobility and Congestion in Dynamical Multilayer Networks with Finite Storage Capacity.
TL;DR: A model in which the nodes have a limited capacity of storing and processing the agents moving over a multilayer network, and their congestions trigger temporary faults which, in turn, dynamically affect the routing of agents seeking for uncongested paths is introduced.
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Hunter-gatherer multilevel sociality accelerates cumulative cultural evolution
Andrea Bamberg Migliano,Andrea Bamberg Migliano,Federico Battiston,Sylvain Viguier,Abigail E. Page,Mark Dyble,Rodolph Schlaepfer,Daniel R. Smith,Leonora Astete,Marilyn Ngales,Jesús Gómez-Gardeñes,Jesús Gómez-Gardeñes,Vito Latora,Lucio Vinicius,Lucio Vinicius +14 more
TL;DR: It is demonstrated that multilevel sociality accelerates cultural differentiation and cumulative cultural evolution in hunter-gatherers by simulating the accumulation of cultural innovations over the real Agta multicamp networks.
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Social Cohesion, Structural Holes, and a Tale of Two Measures
TL;DR: This paper proposes a new measure, Simmelian brokerage, that captures opportunities of brokerage between otherwise disconnected cohesive groups of contacts and shows that clustering and effective size are simply two sides of the same coin.
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Nonlinear growth and condensation in multiplex networks.
TL;DR: A general class of growth models in which the various layers of a multiplex network coevolve through a set of nonlinear preferential attachment rules allow for the appearance of a condensed state in which one node in each layer attracts an extensive fraction of all the edges.
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Networks of motifs from sequences of symbols.
Roberta Sinatra,Roberta Sinatra,Daniele F. Condorelli,Daniele F. Condorelli,Vito Latora,Vito Latora +5 more
TL;DR: The analysis of communities of networks of motifs is shown to be able to correlate sequences with functions in the human proteome database, to detect hot topics from online social dialogs, to characterize trajectories of dynamical systems, and it might find other useful applications to process large amounts of data in various fields.