Opinion competition dynamics on multiplex networks

Amato, Roberta; Kouvaris, Nikos E.; Miguel, M. San; Dı́az-Guilera, Albert

doi:10.1088/1367-2630/aa936a

Cited by 41 publications

(29 citation statements)

References 37 publications

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“…This model gives binary states to a node and each time a randomly chosen node updates its opinion into that of its randomly chosen neighbor. The voter model-type processes were studied in the multilayer network perspective [19,20], in which individuals tend to maintain the same opinion in different layers through interlayer links. It was found that there could be coexistence phase of two states.…”

Section: Introductionmentioning

confidence: 99%

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Majority-vote dynamics on multiplex networks with two layers

Choi

Goh

2019

New J. Phys.

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Majority-vote model is a much-studied model for social opinion dynamics of two competing opinions. With the recent appreciation that our social network comprises a variety of different 'layers' forming a multiplex network, a natural question arises on how such multiplex interactions affect the social opinion dynamics and consensus formation. Here, the majority-vote processes will be studied on multiplex networks with two layers to understand the effect of multiplexity on opinion dynamics. We will discuss how global consensus is reached by different types of voters: AND-and OR-rule voters on multiplex-network and voters on single-network system. The AND-model reaches the largest consensus below the critical noise parameter q c . It needs, however, much longer time to reach consensus than other models. In the vicinity of the transition point, the consensus collapses abruptly. The OR-model attains smaller level of consensus than the AND-rule but reaches the consensus more quickly. Its consensus transition is continuous. The numerical simulation results are supported qualitatively by analytical calculations based on the approximate master equation.

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Section: Introductionmentioning

confidence: 99%

“…Also shown is the result of KGS model of[27]. Simulations are done with networks of N=10 5 and with MC time steps of t final =2 16 -220 . Both the symbols and the connecting lines are the Monte Carlo simulation results.…”

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confidence: 99%

Majority-vote dynamics on multiplex networks with two layers

Choi

Goh

2019

New J. Phys.

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“…Multilayer networks are a framework not only for a better description of complex systems but also for novel dynamical processes that cannot be captured in a single layer framework [9,[13][14][15]. Indeed, several studies on multilayer networks show that interlayer connections account for significant differences in many phenomena, including percolation [10,16,17], diffusion [18], epidemic spreading [19][20][21][22], cascade of failures [11,23], opinion formation [15,[24][25][26], online communities [27], game theory [28][29][30] or cultural dynamics [31].…”

Section: Introductionmentioning

confidence: 99%

Multilayer coevolution dynamics of the nonlinear voter model

Min

Miguel

2019

New J. Phys.

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We study a coevolving nonlinear voter model (CNVM) on a two-layer network. Coevolution stands for coupled dynamics of the state of the nodes and of the topology of the network in each layer. The plasticity parameter p measures the relative time scale of the evolution of the states of the nodes and the evolution of the network by link rewiring. Nonlinearity of the interactions is taken into account through a parameterq that describes the nonlinear effect of local majorities being q=1 the marginal situation of the ordinary voter model. Finally the connection between the two layers is measured by a degree of multiplexing ℓ. In terms of these three parameters,p, q and ℓ we find a rich phase diagram with different phases and transitions. When the two layers have the same plasticity p, the fragmentation transition observed in a single layer is shifted to larger values of p plasticity, so that multiplexing avoids fragmentation. Different plasticities for the two layers lead to new phases that do not exist in a CNVM in a single layer, namely an asymmetric fragmented phase for q>1 and an active shattered phase for q<1. Coupling layers with different types of nonlinearity, q 1 <1 and q 2 >1, we can find two different transitions by increasing the plasticity parameter, a first absorbing transition with no fragmentation and a subsequent fragmentation transition.

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“…Furthermore, adapting the network topology has also successfully been used to control cluster synchronization in delay-coupled networks [34].Another focus of recent research in network science are multilayer networks, which are systems interconnected through different types of links [35][36][37][38]. A prominent example are social networks which can be described as groups of people with different patterns of contacts or interactions between them [39][40][41]. Other applications are communication, supply, and transportation networks, for instance power grids, subway networks, or airtraffic networks [42].…”

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confidence: 99%

Birth and Stabilization of Phase Clusters by Multiplexing of Adaptive Networks

2020

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We propose a concept to generate and stabilize diverse partial synchronization patterns (phase clusters) in adaptive networks which are widespread in neuro-and social sciences, as well as biology, engineering, and other disciplines. We show by theoretical analysis and computer simulations that multiplexing in a multi-layer network with symmetry can induce various stable phase cluster states in a situation where they are not stable or do not even exist in the single layer. Further, we develop a method for the analysis of Laplacian matrices of multiplex networks which allows for insight into the spectral structure of these networks enabling a reduction to the stability problem of single layers. We employ the multiplex decomposition to provide analytic results for the stability of the multilayer patterns. As local dynamics we use the paradigmatic Kuramoto phase oscillator, which is a simple generic model and has been successfully applied in the modeling of synchronization phenomena in a wide range of natural and technological systems.Complex networks are an ubiquitous paradigm in nature and technology, with a wide field of applications ranging from physics, chemistry, biology, neuroscience, to engineering and socio-economic systems. Of particular interest are adaptive networks, where the connectivity changes in time, for instance, the synaptic connections between neurons are adapted depending on the relative timing of neuronal spiking [1][2][3][4][5]. Similarly, chemical systems have been reported [6], where the reaction rates adapt dynamically depending on the variables of the system. Activity-dependent plasticity is also common in epidemics [7] and in biological or social systems [8]. Synchronization is an important feature of the dynamics in networks of coupled nonlinear oscillators [9][10][11][12][13]. Various synchronization patterns are known, like cluster synchronization where the network splits into groups of synchronous elements [14], or partial synchronization patterns like chimera states where the system splits into coexisting domains of coherent (synchronized) and incoherent (desynchronized) states [15][16][17]. These patterns were also explored in adaptive networks [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. Furthermore, adapting the network topology has also successfully been used to control cluster synchronization in delay-coupled networks [34].Another focus of recent research in network science are multilayer networks, which are systems interconnected through different types of links [35][36][37][38]. A prominent example are social networks which can be described as groups of people with different patterns of contacts or interactions between them [39][40][41]. Other applications are communication, supply, and transportation networks, for instance power grids, subway networks, or airtraffic networks [42]. In neuroscience, multilayer networks represent for instance neurons in different areas of the brain, neurons connected either by a chemical link or by an electrical synapsis, or...

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Opinion competition dynamics on multiplex networks

Cited by 41 publications

References 37 publications

Majority-vote dynamics on multiplex networks with two layers

Majority-vote dynamics on multiplex networks with two layers

Multilayer coevolution dynamics of the nonlinear voter model

Birth and Stabilization of Phase Clusters by Multiplexing of Adaptive Networks

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