Network Coherence in a Family of Book Graphs

Chen, Jing; Li, Yifan; Sun, Weigang

doi:10.3389/fphy.2020.583603

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…J. Chen et al proposed graph operation method to construct the network models of book graph on the basis of star networks, and analyzed the influence of network internal parameters on its coherence. It is found that the more nodes in the star graph, the better the consensus of the book graph network [22]. D. Huang et al studied the Laplacian spectrum of several double-layer star-like networks, and analyzed and compared the coherence of these networks [23].…”

Section: Introductionmentioning

confidence: 99%

Coherence Analysis of Symmetric Star Topology Networks

Gao¹,

Zhu

Chen

et al. 2022

Front. Phys.

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The dynamics of complex networks are closely related to the topological structure. As an important research branch, the problem of network consensus has attracted more attention. In this paper, the first-order coherence of three kinds of symmetric star topology networks are studied by using the theory of network science. Firstly, three kinds of symmetric star topology network models are given. Secondly, the first-order coherence of these networks are calculated by using matrix theory. The relationships among the first-order coherence of the network and branch length and the number of branches change are obtained by numerical simulation. Finally, we found that the third network has the best consensus, and the change of branch length has more effective impact on network consensus.

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Section: Introductionmentioning

confidence: 99%

Coherence Analysis of Symmetric Star Topology Networks

Gao¹,

Zhu

Chen

et al. 2022

Front. Phys.

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“…With the development of network science, the research of complex networks has been extended to many fields, such as technical networks and transportation networks. Nowadays, the relevant theoretical knowledge of complex networks has been widely used in physics, computer science, life science, and other fields, such as consensus [1][2][3][4][5], resistance distance and Kirchhoff index [6], robustness [7,8], and network synchronization [9,10].…”

Section: Introductionmentioning

confidence: 99%

Consensus analysis of the weighted corona networks

Zhu

Gao³

et al. 2022

Front. Phys.

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The consensus of complex networks has attracted the attention of many scholars. The graph operation is a common method to construct complex networks, which is helpful in studying the consensus of complex networks. Based on the corona networks G1◦G2, this study gives different weights to the edges of G1◦G2 to obtain the weighted corona networks G̃1◦G̃2 and studies the consensus of G̃1◦G̃2. The consensus of the networks can be measured by coherence. First, the Laplacian polynomial of G̃1◦G̃2 is derived by using the properties of an orthogonal matrix. Second, the relationship between the first-order coherence of G̃1◦G̃2 and G1 is deduced by using the relevant properties of the determinant and the conclusion of polynomial coefficients and the principal minors of the matrix. Third, the join operation is introduced to further simplify the analytical formula of network coherence. Finally, a specific network example is used to verify the validity of the conclusion.

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Strain-induced ferromagnetism and magneto-electric coupling in two-dimensional ferroelectric ZnIn2S4

Li,

Huangfu,

Zhang

et al. 2024

Applied Physics Letters

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Two-dimensional (2D) multiferroic materials are currently in high demand due to their significant potential for applications in the field of high-density data storage devices. However, due to the different requirements for generating ferroelectricity and magnetism, 2D multiferroic materials are rare. In this study, we propose that applying strain can induce magnetism in 2D ferroelectric materials with special electronic structures, thereby creating 2D multiferroics. Taking 2D ZnIn2S4 as an example, it shows robust ferroelectricity with an appropriate switching barrier (79.3 meV), and the out-of-plane ferroelectric polarization is 0.0322 C/m2. Applying biaxial tensile strain can change the energy of the flatband near the Fermi level, ultimately resulting in self-doping phenomena and leading to Stoner-type itinerant ferromagnetism. The reversal of ferroelectric polarization in ZnIn2S4 bilayer and ZnIn2S4–In2Se3 heterostructure can manipulate the magnetic moment of the system, exhibiting significant magnetoelectric coupling phenomena. Our findings provide a pathway for designing 2D ferromagnetic and multiferroic materials.

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Network Coherence in a Family of Book Graphs

Cited by 3 publications

References 26 publications

Coherence Analysis of Symmetric Star Topology Networks

Coherence Analysis of Symmetric Star Topology Networks

Consensus analysis of the weighted corona networks

Strain-induced ferromagnetism and magneto-electric coupling in two-dimensional ferroelectric ZnIn2S4

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