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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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Symmetry induced heteroclinic cycles in a co2 laser

TL;DR: The conditions for the existence of heteroclinic connections between the transverse modes of a CO2 laser whose setup has a perfect cylindrical symmetry are discussed by symmetry arguments for the cases of three, four and five interacting modes.
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Functional Hubs in Mild Cognitive Impairment

TL;DR: In this paper, the authors investigated how hubs of functional brain networks are modified as a result of mild cognitive impairment (MCI), a condition causing a slight but noticeable decline in cognitive abilities, which sometimes precedes the onset of Alzheimer's disease.
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Controlling symmetries and clustered dynamics of complex networks

TL;DR: In this article, the authors proposed an approach to perturb the original network connectivity, either by adding new edges or by adding/removing links together with modifying their weights, to enforce patterned states of synchronization with nodes in a desired way.
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Boundary dominated versus bulk dominated regime in optical space-time complexity

TL;DR: By increasing the aspect ratio of an optical cavity with a photorefractive crystal, the authors in this article observed the transition from a boundary-controlled regime, where the size of the transverse patterns scales with the aspect ratios, to a bulk controlled regime where the pattern size is independent of aspect ratio.
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Predicting transitions in cooperation levels from network connectivity

TL;DR: In this paper, the authors use the unique sequence of degrees in a network to predict at which game parameters major shifts in the level of cooperation can be expected, including phase transitions from absorbing to mixed strategy phases.