46 research outputs found
Synchronization of interconnected networks: the role of connector nodes
In this Letter we identify the general rules that determine the
synchronization properties of interconnected networks. We study analytically,
numerically and experimentally how the degree of the nodes through which two
networks are connected influences the ability of the whole system to
synchronize. We show that connecting the high-degree (low-degree) nodes of each
network turns out to be the most (least) effective strategy to achieve
synchronization. We find the functional relation between synchronizability and
size for a given network-of-networks, and report the existence of the optimal
connector link weights for the different interconnection strategies. Finally,
we perform an electronic experiment with two coupled star networks and conclude
that the analytical results are indeed valid in the presence of noise and
parameter mismatches.Comment: Accepted for publication in Physical Review Letters. Main text: 5
pages, 4 figures. Supplemental material: 8 pages, 3 figure
Dynamics of modal power distribution in a multimode semiconductor laser with optical feedback
The dynamics of power distribution between longitudinal modes of a multimode
semiconductor laser subjected to external optical feedback is experimentally
analyzed in the low-frequency fluctuation regime. Power dropouts in the total
light intensity are invariably accompanied by sudden activations of several
longitudinal modes. These activations are seen not to be simultaneous to the
dropouts, but to occur after them. The phenomenon is statistically analysed in
a systematic way, and the corresponding delay is estimated.Comment: 3 pages, 4 figures, revte
Using network science to analyze football passing networks: dynamics, space, time and the multilayer nature of the game
From the diversity of applications of Network Science, in this Opinion Paper
we are concerned about its potential to analyze one of the most extended group
sports: Football (soccer in U.S. terminology). As we will see, Network Science
allows addressing different aspects of the team organization and performance
not captured by classical analyses based on the performance of individual
players. The reason behind relies on the complex nature of the game, which,
paraphrasing the foundational paradigm of complexity sciences "can not be
analyzed by looking at its components (i.e., players) individually but, on the
contrary, considering the system as a whole" or, in the classical words of
after-match interviews "it's not just me, it's the team".Comment: 7 pages, 1 figur
Topological Measure Locating the Effective Crossover between Segregation and Integration in a Modular Network
We introduce an easily computable topological measure which locates the
effective crossover between segregation and integration in a modular network.
Segregation corresponds to the degree of network modularity, while integration
is expressed in terms of the algebraic connectivity of an associated
hyper-graph. The rigorous treatment of the simplified case of cliques of equal
size that are gradually rewired until they become completely merged, allows us
to show that this topological crossover can be made to coincide with a
dynamical crossover from cluster to global synchronization of a system of
coupled phase oscillators. The dynamical crossover is signaled by a peak in the
product of the measures of intra-cluster and global synchronization, which we
propose as a dynamical measure of complexity. This quantity is much easier to
compute than the entropy (of the average frequencies of the oscillators), and
displays a behavior which closely mimics that of the dynamical complexity index
based on the latter. The proposed toplogical measure simultaneously provides
information on the dynamical behavior, sheds light on the interplay between
modularity vs total integration and shows how this affects the capability of
the network to perform both local and distributed dynamical tasks
Explosive first-order transition to synchrony in networked chaotic oscillators
Critical phenomena in complex networks, and the emergence of dynamical abrupt
transitions in the macroscopic state of the system are currently a subject of
the outmost interest. We report evidence of an explosive phase synchronization
in networks of chaotic units. Namely, by means of both extensive simulations of
networks made up of chaotic units, and validation with an experiment of
electronic circuits in a star configuration, we demonstrate the existence of a
first order transition towards synchronization of the phases of the networked
units. Our findings constitute the first prove of this kind of synchronization
in practice, thus opening the path to its use in real-world applications.Comment: Phys. Rev. Lett. in pres
Synchronization interfaces and overlapping communities in complex networks
We show that a complex network of phase oscillators may display interfaces
between domains (clusters) of synchronized oscillations. The emergence and
dynamics of these interfaces are studied in the general framework of
interacting phase oscillators composed of either dynamical domains (influenced
by different forcing processes), or structural domains (modular networks). The
obtained results allow to give a functional definition of overlapping
structures in modular networks, and suggest a practical method to identify
them. As a result, our algorithm could detect information on both single
overlapping nodes and overlapping clusters.Comment: 5 pages, 4 figure
Episodic synchronization in dynamically driven neurons
We examine the response of type II excitable neurons to trains of synaptic
pulses, as a function of the pulse frequency and amplitude. We show that the
resonant behavior characteristic of type II excitability, already described for
harmonic inputs, is also present for pulsed inputs. With this in mind, we study
the response of neurons to pulsed input trains whose frequency varies
continuously in time, and observe that the receiving neuron synchronizes
episodically to the input pulses, whenever the pulse frequency lies within the
neuron's locking range. We propose this behavior as a mechanism of rate-code
detection in neuronal populations. The results are obtained both in numerical
simulations of the Morris-Lecar model and in an electronic implementation of
the FitzHugh-Nagumo system, evidencing the robustness of the phenomenon.Comment: 7 pages, 8 figure
Modeling the evolution of item rating networks using time-domain preferential attachment
The understanding of the structure and dynamics of the intricate network of connections among people that consumes products through Internet appears as an extremely useful asset in order to study emergent properties related to social behavior. This knowledge could be useful, for example, to improve the performance of personal recommendation algorithms. In this contribution, we analyzed five-year records of movie-rating transactions provided by Netflix, a movie rental platform where users rate movies from an online catalog. This dataset can be studied as a bipartite user-item network whose structure evolves in time. Even though several topological properties from subsets of this bipartite network have been reported with a model that combines random and preferential attachment mechanisms [Beguerisse DÃaz et al., 2010], there are still many aspects worth to be explored, as they are connected to relevant phenomena underlying the evolution of the network. In this work, we test the hypothesis that bursty human behavior is essential in order to describe how a bipartite user-item network evolves in time. To that end, we propose a novel model that combines, for user nodes, a network growth prescription based on a preferential attachment mechanism acting not only in the topological domain (i.e. based on node degrees) but also in time domain. In the case of items, the model mixes degree preferential attachment and random selection. With these ingredients, the model is not only able to reproduce the asymptotic degree distribution, but also shows an excellent agreement with the Netflix data in several time-dependent topological properties