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.
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Dynamic interdependence and competition in multilayer networks
TL;DR: In this paper, the authors define a dynamic dependency link and develop a general framework for interdependent and competitive interactions between general dynamic systems and apply their framework to studying interdependent synchronization in multi-layer oscillator networks and cooperative/competitive contagions in an epidemic model.
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Explosive synchronization in populations of cooperative and competitive oscillators
Xiangfeng Dai,Xuelong Li,Ricardo Gutiérrez,Hao Guo,Danyang Jia,Matjaž Perc,Pouya Manshour,Zhen Wang,Stefano Boccaletti,Stefano Boccaletti +9 more
TL;DR: It is reported that in populations of cooperative and competitive oscillators the transition can be tuned between continuous and explosive simply by adjusting the balance between the two oscillator types.
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Experimental phase synchronization of a chaotic convective flow.
TL;DR: Experimental evidence of phase synchronization of high dimensional chaotic oscillators in a laboratory experiment that consists of a thermocapillary driven convective cell in a time dependent chaotic regime is reported.
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Adaptive Control of Chaos
Stefano Boccaletti,F. T. Arecchi +1 more
TL;DR: In this article, an adaptive method for stabilizing the unstable periodic orbits embedded in a chaotic attractor is proposed, based on a continuous correction of the dynamics with a forcing term driven by the local information extracted from the dynamics itself.
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The formation of synchronization cliques during the development of modular neural networks
TL;DR: This study uses simulations of biologically inspired neuronal networks during development to study the formation of functional groups (cliques) and inter-neuronal synchronization and shows that by the local synchronization properties at the very early developmental stages, it is possible to predict with high accuracy which clusters will become dominant in later stages of network development.