Interplay between social influence and competitive strategical games in multiplex networks

Amato, Roberta; Dı́az-Guilera, Albert; Kleineberg, Kaj-Kolja

doi:10.1038/s41598-017-06933-2

Cited by 28 publications

(24 citation statements)

References 39 publications

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“…Specifically, from Eqs. (29), (32) we see that for each pair of nodes i, j in layer 1 we can assign a score:…”

Section: Improving Trans-layer Link Predictionmentioning

confidence: 99%

“…Eqs. (29), (32) suggest that ψ(x 1 ) is given by Eq. (32), which requires knowledge of the conditional density f (x 2 |x 1 ) and the connection probability p 2 (x 2 ).…”

Section: Improving Trans-layer Link Predictionmentioning

confidence: 99%

“…tection and trans-layer link prediction on a geometric basis, as well as multilayer greedy routing [14]. Furthermore, it was shown that the discovered correlations play an important role in the robustness of multiplexes against targeted attacks to high degree nodes [31] and to the outcome of evolutionary dynamics [32,33]. Yet, despite these advances, it is still not fully understood to what extend can coordinate correlations alone explain the topological relation between the layers of real systems.…”

Section: Introductionmentioning

confidence: 99%

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Link persistence and conditional distances in multiplex networks

Papadopoulos

Kleineberg

2019

Phys. Rev. E

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Recent progress towards unraveling the hidden geometric organization of real multiplexes revealed significant correlations across the hyperbolic node coordinates in different network layers, which facilitated applications like trans-layer link prediction and mutual navigation. But are geometric correlations alone sufficient to explain the topological relation between the layers of real systems? Here we provide the negative answer to this question. We show that connections in real systems tend to persist from one layer to another irrespectively of their hyperbolic distances. This suggests that in addition to purely geometric aspects the explicit link formation process in one layer impacts the topology of other layers. Based on this finding, we present a simple modification to the recently developed Geometric Multiplex Model to account for this effect, and show that the extended model can reproduce the behavior observed in real systems. We also find that link persistence is significant in all considered multiplexes and can explain their layers' high edge overlap, which cannot be explained by coordinate correlations alone. Furthermore, by taking both link persistence and hyperbolic distance correlations into account we can improve trans-layer link prediction. These findings guide the development of multiplex embedding methods, suggesting that such methods should be accounting for both coordinate correlations and link persistence across layers.A first step towards this direction is the finding that if the layers comprising real multiplexes are independently embedded into hyperbolic spaces, their coordinates exhibit significant correlations [14]. This finding motivated new applications, like multidimensional community de-1 2β r , gives the power law degree distribution, P (k) ≈ ρ(r(k))|r (k)| ∝ k −γ , γ = 1 + 1/β > 2. Without loss of generality, we assume here a hyperbolic plane of curvature K = −1. See [2] for further details.

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“…Specifically, from Eqs. (29), (32) we see that for each pair of nodes i, j in layer 1 we can assign a score:…”

Section: Improving Trans-layer Link Predictionmentioning

confidence: 99%

“…Eqs. (29), (32) suggest that ψ(x 1 ) is given by Eq. (32), which requires knowledge of the conditional density f (x 2 |x 1 ) and the connection probability p 2 (x 2 ).…”

Section: Improving Trans-layer Link Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Link persistence and conditional distances in multiplex networks

Papadopoulos

Kleineberg

2019

Phys. Rev. E

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“…Such interactions can be captured by multiplex networks, which are systems comprised of several network layers, where the same set of individuals are present [17][18][19][20][21]. The impact of multiplexity on the outcome of evolutionary games is of high importance for the emergence and stability of cooperation in real systems and has recently attracted a lot of attention [22][23][24][25][26][27][28][29][30][31][32][33][34]. Especially the interplay between the structural organization of the different domains in the multiplex-for example whether there are correlations between the importance of an individual in the business and social domain-and evolutionary game dynamics is still not well understood.…”

Section: Introductionmentioning

confidence: 99%

Topological enslavement in evolutionary games on correlated multiplex networks

Kleineberg¹,

Helbing²

2018

New J. Phys.

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Governments and enterprises strongly rely on incentives to generate favorable outcomes from social and strategic interactions between individuals. The incentives are usually modeled by payoffs in evolutionary games, such as the prisoners dilemma or the harmony game, with imitation dynamics. Adjusting the incentives by changing the payoff parameters can favor cooperation, as found in the harmony game, over defection, which prevails in the prisoner's dilemma. Here, we show that this is not always the case if individuals engage in strategic interactions in multiple domains. In particular, we investigate evolutionary games on multiplex networks where individuals obtain an aggregate payoff. We explicitly control the strength of degree correlations between nodes in the different layers of the multiplex. We find that if the multiplex is composed of many layers and degree correlations are strong, the topology of the system enslaves the dynamics and the final outcome, cooperation or defection, becomes independent of the payoff parameters. The fate of the system is then determined by the initial conditions. OPEN ACCESS RECEIVED

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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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Interplay between social influence and competitive strategical games in multiplex networks

Cited by 28 publications

References 39 publications

Link persistence and conditional distances in multiplex networks

Link persistence and conditional distances in multiplex networks

Topological enslavement in evolutionary games on correlated multiplex networks

Birth and Stabilization of Phase Clusters by Multiplexing of Adaptive Networks

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