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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Proceedings ArticleDOI
Pattern formation in spatially distributed networks via spatially correlated preferential attachment
TL;DR: This work unite two principles of the real network modeling: the correlated percolation model and preferential attachment, and uses density gradient, which determines the decrease of the probability of the connection emergence between two nodes with increase of the distance between them.
Journal ArticleDOI
Anomalous synchronization of spatially extended chaotic systems in the presence of asymmetric coupling
TL;DR: In this paper, the effects of asymmetric coupling in the synchronization of two spatially extended systems are described, and the consequences induced by the presence of asymmetries in the coupling configuration of a pair of one-dimensional fields obeying Complex Ginzburg-Landau equations.
Proceedings ArticleDOI
Synchronization domains in arrays of chaotic homoclinic systems
TL;DR: In this article, the dynamics of a closed chain of unidirectionally coupled oscillators in a regime of homoclinic chaos was investigated, where two simultaneous forcings were applied at different points of the array, including temporal alternation and spatial coexistence synchronization domains.
Journal ArticleDOI
Distances in Higher-Order Networks and the Metric Structure of Hypergraphs
E. Vasilyeva,Miguel Romance,Ivan Yuryevich Samoylenko,Kirill E. Kovalenko,Daniil Musatov,Andrei Mikhailovich Raigorodskii,Stefano Boccaletti +6 more
TL;DR: In this paper , the authors explore the metric structure of networks with higher-order interactions and introduce a novel definition of distance for hypergraphs that extends the classic methods reported in the literature.
Book ChapterDOI
Chaos in the Brain: A New Strategy to Discriminate Deterministic Low Dimensional Dynamics in the Spontaneous Activity of the Human Cortex
TL;DR: It is likely that this difficulty in defining the “measured system” and the large time variability of the Signal to Noise Ratio (SNR) is one of the reasons for the wide spectrum of results that can be found in the literature on chaotic estimators especially when investigating spontaneous activity.