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Irene Sendiña-Nadal

Researcher at Technical University of Madrid

Publications -  88
Citations -  5526

Irene Sendiña-Nadal is an academic researcher from Technical University of Madrid. The author has contributed to research in topics: Complex network & Synchronization (computer science). The author has an hindex of 28, co-authored 82 publications receiving 4498 citations. Previous affiliations of Irene Sendiña-Nadal include University of Santiago de Compostela & King Juan Carlos University.

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The structure and dynamics of multilayer networks

TL;DR: This work offers a comprehensive review on both structural and dynamical organization of graphs made of diverse relationships (layers) between its constituents, and cover several relevant issues, from a full redefinition of the basic structural measures, to understanding how the multilayer nature of the network affects processes and dynamics.
Journal ArticleDOI

The structure and dynamics of multilayer networks

TL;DR: In this article, a comprehensive review on both structural and dynamical organization of graphs made of diverse relationships (layers) between its constituents, and cover several relevant issues, from a full redefinition of the basic structural measures, to understanding how the multilayer nature of the network affects processes and dynamics.
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Explosive transitions in complex networks’ structure and dynamics: Percolation and synchronization

TL;DR: In this paper, a review of the main-stream literature on phase transitions in networked systems is presented, with the twofold aim of summarizing the existing results and pointing out possible directions for future research.
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Explosive First-Order Transition to Synchrony in Networked Chaotic Oscillators

TL;DR: This work demonstrates the existence of a first-order transition towards synchronization of the phases of the networked units, the first prove of this kind of synchronization in practice, thus opening the path to its use in real-world applications.
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Reorganization of functional networks in mild cognitive impairment.

TL;DR: Due to the high rate of conversion from MCI to Alzheimer Disease (AD), the analysis of functional networks could be an appropriate tool for the early detection of both MCI and AD.