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
More filters
Journal ArticleDOI
Detecting complex network modularity by dynamical clustering
TL;DR: Based on cluster desynchronization properties of phase oscillators, an efficient method is introduced for the detection and identification of modules in complex networks with a high level of precision.
Journal ArticleDOI
Street Centrality and the Location of Economic Activities in Barcelona
Sergio Porta,Vito Latora,Fahui Wang,Fahui Wang,Salvador Rueda,Emanuele Strano,Salvatore Scellato,Allessio Cardillo,Eugenio Belli,Francisco Cardenas,Berta Cormenzana,Laura Latora +11 more
TL;DR: In this paper, the authors examined the geography of three street centrality indices and their correlations with various types of economic activities in Barcelona, Spain and found that the correlation is higher with secondary than primary activities.
Journal ArticleDOI
Measuring and modeling correlations in multiplex networks.
Vincenzo Nicosia,Vito Latora +1 more
TL;DR: This work introduces various measures to characterize correlations in the activity of the nodes and in their degree at the different layers and between activities and degrees and shows that real-world networks exhibit indeed nontrivial multiplex correlations.
Book ChapterDOI
Graph Metrics for Temporal Networks
TL;DR: This chapter discusses how to represent temporal networks and the definitions of walks, paths, connectedness and connected components valid for graphs in which the links fluctuate over time, and focuses on temporal node–node distance.
Proceedings Article
Distance matters: geo-social metrics for online social networks
TL;DR: This paper presents a graph analysis based approach to study social networks with geographic information and new metrics to characterize how geographic distance affects social structure, and demonstrates that different social networking services exhibit different geo-social properties.