Influence of Layer Similarity on the Synchronization of Multiplex Networks With Random Topologies

Yin, Xiangxin; Xiao, Rui; Dai, Haifeng; Zhu, Quanxin; Sun, Yongzheng

doi:10.1109/tsmc.2023.3290674

Cited by 4 publications

(3 citation statements)

References 46 publications

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“…Some scholars have explored what conditions of the adjacency matrix (topology) of CDNs are most beneficial for synchronization to occur. For example, the mathematical expressions of the occurrence of synchronization phenomena have been derived under constant topologies [4][5][6][7], switching topologies [8][9][10][11], and random topologies [12][13][14]. In Refs.…”

Section: Introductionmentioning

confidence: 99%

“…In Refs. [12][13][14], the connection or disconnection between nodes followed a given random probability distribution; therefore, exploring the network size and information dissemination patterns can help realize synchronization in such a scenario. Another group of scholars has utilized optimization methods and models of CDNs to seek an optimal topology that can promote synchronization [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…By analyzing the literature, the following two noticeable gaps emerge: (1) In the existing research on CDN synchronization control, the synchronization mechanism of CDNs with dynamic edges is largely ignored [4][5][6][7][8][9][10][11][12][13][14][15][16][17]. This neglect is reflected not only in the dynamic edges' characteristics but also in the impact of edges on node states.…”

Section: Introductionmentioning

confidence: 99%

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Synchronization Mechanism for Controlled Complex Networks under Auxiliary Effect of Dynamic Edges

Liu,

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The scope of complex dynamical networks (CDNs) with dynamic edges is very wide, as it is composed of a class of realistic networks including web-winding systems, communication networks, neural networks, etc. However, a classic research topic in CDNs, the synchronization control problem, has not been effectively solved for CDNs with dynamic edges. This paper will investigate the emergence mechanism of synchronization from the perspective of large-scale systems. Firstly, a CDN with dynamic edges is conceptualized as an interconnected coupled system composed of an edge subsystem (ES) and a node subsystem (NS). Then, based on the proposed improved directed matrix ES model and expanded matrix inequality, this paper overcomes the limitations of coupling term design in node models and the strong correlation of tracking targets between nodes and edges. Due to the effect of the synthesized node controller and the auxiliary effect of the ES, state synchronization can be realized in the CDN. Finally, through simulation examples, the validity and advantages of our work compared to existing methods are demonstrated.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Synchronization Mechanism for Controlled Complex Networks under Auxiliary Effect of Dynamic Edges

Liu,

Gao,

Peng

2024

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Dynamic modulation of external excitation enhance synchronization in complex neuronal network

Wu,

Ding,

Huang

et al. 2024

Chaos, Solitons & Fractals

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On the higher-order smallest ring-star network of Chialvo neurons under diffusive couplings

Nair,

Ghosh,

Fatoyinbo

et al. 2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Network dynamical systems with higher-order interactions are a current trending topic, pervasive in many applied fields. However, our focus in this work is neurodynamics. We numerically study the dynamics of the smallest higher-order network of neurons arranged in a ring-star topology. The dynamics of each node in this network is governed by the Chialvo neuron map, and they interact via linear diffusive couplings. This model is perceived to imitate the nonlinear dynamical properties exhibited by a realistic nervous system where the neurons transfer information through multi-body interactions. We deploy the higher-order coupling strength as the primary bifurcation parameter. We start by analyzing our model using standard tools from dynamical systems theory: fixed point analysis, Jacobian matrix, and bifurcation patterns. We observe the coexistence of disparate chaotic attractors. We also observe an interesting route to chaos from a fixed point via period-doubling and the appearance of cyclic quasiperiodic closed invariant curves. Furthermore, we numerically observe the existence of codimension-1 bifurcation points: saddle-node, period-doubling, and Neimark–Sacker. We also qualitatively study the typical phase portraits of the system, and numerically quantify chaos and complexity using the 0–1 test and sample entropy measure, respectively. Finally, we study the synchronization behavior among the neurons using the cross correlation coefficient and the Kuramoto order parameter. We conjecture that unfolding these patterns and behaviors of the network model will help us identify different states of the nervous system, further aiding us in dealing with various neural diseases and nervous disorders.

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Influence of Layer Similarity on the Synchronization of Multiplex Networks With Random Topologies

Cited by 4 publications

References 46 publications

Synchronization Mechanism for Controlled Complex Networks under Auxiliary Effect of Dynamic Edges

Synchronization Mechanism for Controlled Complex Networks under Auxiliary Effect of Dynamic Edges

Dynamic modulation of external excitation enhance synchronization in complex neuronal network

On the higher-order smallest ring-star network of Chialvo neurons under diffusive couplings

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