S
Stefano Boccaletti
Researcher at Moscow Institute of Physics and Technology
Publications - 361
Citations - 29686
Stefano Boccaletti is an academic researcher from Moscow Institute of Physics and Technology. The author has contributed to research in topics: Complex network & Synchronization (computer science). The author has an hindex of 60, co-authored 348 publications receiving 25776 citations. Previous affiliations of Stefano Boccaletti include King Juan Carlos University & Istituto Nazionale di Fisica Nucleare.
Papers
More filters
Journal ArticleDOI
Growing hierarchical scale-free networks by means of nonhierarchical processes
TL;DR: A fully nonhierarchical network growing mechanism, that furthermore does not impose explicit preferential attachment rules, that produces a graph featuring power-law degree and clustering distributions, and manifesting slightly disassortative degree-degree correlations.
Journal ArticleDOI
Unveiling protein functions through the dynamics of the interaction network.
Irene Sendiña–Nadal,Yanay Ofran,Juan A. Almendral,Javier M. Buldú,Inmaculada Leyva,Daqing Li,Shlomo Havlin,Stefano Boccaletti +7 more
TL;DR: The inspection of the properties of oscillatory dynamics on top of the protein interaction network leads to the identification of misclassification problems in protein function assignments, as well as to unveil correct identification of protein functions.
Journal ArticleDOI
Multiple peaks patterns of epidemic spreading in multi-layer networks.
TL;DR: It is shown that the essential ingredient is a weak coupling condition between the layers themselves, while different degree distributions in the two layers are also helpful, and an edge-based theory is developed which fully explains all numerical results.
Journal ArticleDOI
Synchronization in dynamical networks with unconstrained structure switching
Charo I. Del Genio,Miguel Romance,Miguel Romance,Regino Criado,Regino Criado,Stefano Boccaletti +5 more
TL;DR: This work provides a rigorous solution to the problem of constructing a structural evolution for a network of coupled identical dynamical units that switches between specified topologies without constraints on their structure.
Journal ArticleDOI
Complex networks analysis of obstructive nephropathy data.
TL;DR: The topology of complex networks created upon vectors of features for control and ON subjects is related with the severity of the pathology, and resulting topologies allow discriminating ON subjects and detecting which genetic or metabolic elements are responsible for the malfunction.