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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Journal ArticleDOI
The shape of memory in temporal networks
TL;DR: In this article , the authors introduce a mathematical framework for defining and efficiently estimating the microscopic shape of memory, which characterises how the activity of each link intertwines with the activities of all other links.
Posted Content
Nonlinear walkers and efficient exploration of congested networks
TL;DR: Using the entropy rate the paper shows that an optimal crowding amount exists that maximizes the ability of the walkers to perform the network exploration.
Journal ArticleDOI
Mega et al. Reply
Mirko S. Mega,Paolo Allegrini,Paolo Grigolini,Vito Latora,Luigi Palatella,Andrea Rapisarda,Sergio Vinciguerra +6 more
TL;DR: A reply to a comment by A. Helmstetter and D. Sornette about the article 'Power-Law Time Distribution of Large Earthquakes' from 2003 is given in this article.
Book Chapter
Correlating densities of centrality and activities in cities: the cases of Bologna (IT) and Barcelona (ES)
Sergio Porta,Vito Latora,Feng Wang,Salvador Rueda,Berta Cormenzana,Francisco Cardenas,L. Latora,Emanuele Strano,E. Belli,Alessio Cardillo,Salvatore Scellato +10 more
TL;DR: In this article, the authors examined the relationship between street centrality and densities of commercial and service activities in cities and found that secondary activities exhibit a higher correlation with centrality.
Journal ArticleDOI
Predicting urban innovation from the US Workforce Mobility Network
TL;DR: In this article, the authors investigated the impact of social network structures on the innovation performance of cities and found that node centrality computed on this network accounts for most of the variability observed in cities' innovation performance and significantly outperforms other predictors such as population size or density.