Reciprocal interactions out of congestion-free adaptive networks

Gavalda, Arnau; Duch, Jordi; Gómez‐Gardeñes, Jesús

doi:10.1103/physreve.85.026112

Cited by 4 publications

(6 citation statements)

References 37 publications

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“…Hence, R = 1 for a purely bidirectional link, and R = 0 for a unidirectional one. Considering the weighted nature of contact networks 13 22 41 , we define the reciprocity of communications between G 0 and G 1 users as where V x→y is the number of calls from user x to user y within a given period, and |·| is the absolute value. With this definition, two users have a reciprocity ranging from 0 to 1 , where R = 1 corresponds to reciprocal links, and R = 0 for non-reciprocal ones.…”

Section: Resultsmentioning

confidence: 99%

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Quantifying Information Flow During Emergencies

Gao

et al. 2014

Sci Rep

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Recent advances on human dynamics have focused on the normal patterns of human activities, with the quantitative understanding of human behavior under extreme events remaining a crucial missing chapter. This has a wide array of potential applications, ranging from emergency response and detection to traffic control and management. Previous studies have shown that human communications are both temporally and spatially localized following the onset of emergencies, indicating that social propagation is a primary means to propagate situational awareness. We study real anomalous events using country-wide mobile phone data, finding that information flow during emergencies is dominated by repeated communications. We further demonstrate that the observed communication patterns cannot be explained by inherent reciprocity in social networks, and are universal across different demographics.

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Section: Resultsmentioning

confidence: 99%

“…Hence, R = 1 for a purely bidirectional link, and R = 0 for a unidirectional one. Considering the weighted nature of contact networks 13,22,41 , we define the reciprocity of communications between G 0 and G 1 users as…”

Section: Resultsmentioning

confidence: 99%

Quantifying Information Flow During Emergencies

Gao

et al. 2014

Sci Rep

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“…Next to the active interest in adaptive networks with regards to neuroscience, motivation for systems with adaptive connectivity structure has been found in other dynamical systems from biology [PAI14] and social science [SAY13, SAY15,AOK16], in systems that describe swarming behavior of coupled agents [IWA10,IWA10a], or in the dynamics of communication networks [GAV12]. Furthermore, it has been shown that synchronization in complex networks is enhanced by an adaptation of the coupling structure [ZHO06f,ZHU10].…”

Section: Dynamics Of Complex Networkmentioning

confidence: 99%

Patterns of Synchrony in Complex Networks of Adaptively Coupled Oscillators

Berner¹

2021

Springer Theses

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Collective phenomena in systems of interconnected dynamical units are omnipresent in nature. The swarm behavior in flocks of birds or schools of fish, the synchronous flashing of fireflies or even coherent spiking of neurons in the human brain are just a few examples of collective motion. Elucidating the mechanisms that give rise to synchronization is crucial in order to understand biological self-organization. For this sake, the theory of dynamical networks has been successfully applied over the last decades to boil down the complex dynamics from natural systems to their essentials. In addition, dynamical networks with adaptive couplings and hence non-constant network structure appear naturally in real-world systems such as power grid networks, social networks as well as neuronal networks. In this thesis, we study adaptive networks and their properties which give rise to the emergence of a variety of synchronization patterns, including complete and cluster synchronization as well as solitary and chimera-like states. One of the main fields of application that is investigated in this thesis concerns neuroscience. However, the methods and approaches developed in this work are not restricted to a specific field of application.aus gekoppelten Phasenoszillatoren an und zeigen, wie das subtile Zusammenspiel zwischen Adaptivität und Netzwerkstruktur die Entstehung komplexer partieller Synchronisationsmuster hervorruft.Trotz des regen Interesses an adaptiven Netzwerken ist über das Zusammenspiel adaptiv gekoppelter Netzwerkgruppen wenig bekannt. Solche adaptiven Mehrschicht-oder Multiplex-Netzwerke kommen in neuronalen Netzwerken ganz natürlich vor. Wir schlagen ein Konzept zur Erzeugung und Stabilisierung diverser partieller Synchronisationsmuster (Phasen-Cluster) in adaptiven Netzwerken vor. Wir zeigen, dass das "Multiplexen" verschiedene stabile Phasen-Cluster-Zustände induzieren kann, selbst wenn diese in den Einschicht-Systemen nicht stabil sind oder gar nicht existieren. Weiterhin entwickeln wir eine Methode zur Analyse von Laplace-Matrizen von Multiplex-Netzwerken, die einen Einblick in die spektrale Struktur dieser Netzwerke ermöglicht und damit eine Reduktion des Stabilitätsproblems auf die von Einschicht-Netzwerken ermöglicht. Wir setzen die neue Methode ein, um analytische Ergebnisse für die Stabilität der Zustände im Mehrschicht-System zu erhalten.

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“…For such a system of two coupled Hodgkin-Huxley neurons with STDP, the reduction procedure from Sec. III C can be applied numerically by using the averaging method in (17) for sufficiently large T.…”

Section: Reduced Model: Two Hodgkin-huxley Neurons With Spike-timing-...mentioning

confidence: 99%

“…Another adaptation mechanism, structural plasticity, is responsible for a homeostatic regulation of electrical activity in the brain. 16 Beyond neural networks, adaptive networks are used in communication systems 17 and for modeling complex behavior in social, climate, or ecological systems. 5,18,19 In realistic systems, the adaptation rules are likely to be heterogeneous.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric adaptivity induces recurrent synchronization in complex networks

Thiele

Berner

Tass

et al. 2023

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Rhythmic activities that alternate between coherent and incoherent phases are ubiquitous in chemical, ecological, climate, or neural systems. Despite their importance, general mechanisms for their emergence are little understood. In order to fill this gap, we present a framework for describing the emergence of recurrent synchronization in complex networks with adaptive interactions. This phenomenon is manifested at the macroscopic level by temporal episodes of coherent and incoherent dynamics that alternate recurrently. At the same time, the dynamics of the individual nodes do not change qualitatively. We identify asymmetric adaptation rules and temporal separation between the adaptation and the dynamics of individual nodes as key features for the emergence of recurrent synchronization. Our results suggest that asymmetric adaptation might be a fundamental ingredient for recurrent synchronization phenomena as seen in pattern generators, e.g., in neuronal systems.

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Reciprocal interactions out of congestion-free adaptive networks

Cited by 4 publications

References 37 publications

Quantifying Information Flow During Emergencies

Quantifying Information Flow During Emergencies

Patterns of Synchrony in Complex Networks of Adaptively Coupled Oscillators

Asymmetric adaptivity induces recurrent synchronization in complex networks

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