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
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Targeting the dynamics of complex
TL;DR: In this paper, a generic procedure to steer (target) a network's dynamics towards a given, desired evolution is reported. And the problem is tackled through a Master Stability Function approach, assessing the stability of the aimed dynamics, through a selection of nodes to be targeted.
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Localized structures in nonlinear optics: spatial features and interactions
TL;DR: In this paper, the authors studied the localization of spatial dissipative structures in a nonlinear system formed by a liquid crystal light valve closed in an optical feedback loop, and investigated how stability and shape of the observed patterns depend on experimental parameters.
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Mean-field nature of synchronization stability in networks with multiple interaction layers
TL;DR: In this paper , a mean-field theory of synchronization for networks with multiple interaction layers is proposed, assuming quasi-identical layers, which can be used to obtain accurate assessments of synchronization stability that are comparable with the exact results.
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Why are there six degrees of separation in a social network?
Ivan Yuryevich Samoylenko,David Aleja,Eva Primo,K. Alfaro-Bittner,E. Vasilyeva,Kirill E. Kovalenko,Daniil Musatov,Andrei Mikhailovich Raigorodskii,Regino Criado,Miguel Romance,David Papo,Matjaž Perc,Baruch Barzel,Stefano Boccaletti +13 more
TL;DR: In this paper , it was shown that the six degrees of separation is a property of social networks and that it is compatible with clustering and scale-freeness, which normally characterize social networks.
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Generation of scale-free topology in complex networks by phase entrainment
TL;DR: It is shown that successful entrainment always corresponds to the generation of a scale-free topology in the original graph.