Self-similarity of complex networks under centrality-based node removal strategy

Chen, Dan; Cai, Defu; Su, Housheng

doi:10.1088/1674-1056/acd3e2

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…[22] The node with negative centrality is regarded as a peripheral one. [23] For instance, we can calculate the nodes of the centralities in the karate club network [24] by formula (1). The results are listed in order in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Detecting the core of a network by the centralities of the nodes

Ma 马,

Ren 任,

Zhu 朱

et al. 2024

Chinese Phys. B

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Many networks exhibit the core/periphery structure. Core/periphery structure is a type of meso-scale structure that consists of densely connected core nodes and sparsely connected peripheral nodes. Core nodes tend to be well-connected both among themselves and to peripheral nodes which tend not to be well-connected to other nodes. In this brief report, we propose a new method to detect the core of a network by the centrality of each node. It is discovered that such nodes with the non-negative centrality often consist of the core of the networks. The simulation is carried out on the different real networks. And the results could be checked by the objective function. The checked results may show the effectiveness of the simulation results by the centralities of the nodes on the real networks. Furthermore, we discuss the characters of networks with the single core/periphery structure and point out the scope of the application of our method at the end of this report.

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Section: Methodsmentioning

confidence: 99%

Detecting the core of a network by the centralities of the nodes

Ma 马,

Ren 任,

Zhu 朱

et al. 2024

Chinese Phys. B

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“…The results obtained through this model are very consistent with the trend of expensive and time-consuming numerical simulation results. Compared with previous complex and timeconsuming numerical simulation methods in hydrodynamics and atomic dynamics, this analytical method [24][25][26] has the advantages of a clear process and rapid calculation. More importantly, it can reverse a set of required conditions back to the experimental parameters required to produce these conditions by laser-target interaction.…”

Section: Introductionmentioning

confidence: 99%

Quasi-three-dimensional hydrodynamics of the corona region of laser irradiation of a slab

Dong 董,

Guan 关,

Li 李

2024

Chinese Phys. B

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This paper introduces and establishes a quasi-three-dimensional physical model of the interaction between laser and slab target. In contrast to previous one-dimensional analytical models, this paper innovatively fits the real laser conditions, based on an isothermal, homogeneous expansion similarity solution of the ideal hydrodynamic equations, using this simple model, the evolution law and analytical formula of key parameters (e.g., temperature, density, and scale length) in the corona region under certain conditions are given. The analytical solution agrees well with the relevant computational hydrodynamics simulation results. For constant laser irradiation, the analytical solution provides a meaningful power-law scaling relationship. The model provides a set of mathematical and physical tools that provide theoretical support for adjusting parameters in experiments.

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Communicability cosine distance: similarity and symmetry in graphs/networks

Estrada

2024

Comp. Appl. Math.

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A distance based on the exponential kernel of the adjacency matrix of a graph and representing how well two vertices connect to each other in a graph is defined and studied. This communicability cosine distance (CCD) is a Euclidean spherical distance accounting for the cosine of the angles spanned by the position vectors of the graph vertices in this space. The Euclidean distance matrix (EDM) of CCD is used to quantify the similarity between vertices in graphs and networks as well as to define a local vertex invariant—a closeness centrality measure, which discriminate very well vertices in small graphs. It allows to distinguish all nonidentical vertices, also characterizing all identity (asymmetric) graphs–those having only the identity automorphism–among all connected graphs of up to 9 vertices. It also characterizes several other classes of identity graphs. We also study real-world networks in term of both the discriminating power of the new centrality on their vertices as well as in ranking their vertices. We analyze some dictionary networks as well as the network of copurshasing of political books, remarking some of the main advantages of the new approaches studied here.

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Self-similarity of complex networks under centrality-based node removal strategy

Cited by 3 publications

References 31 publications

Detecting the core of a network by the centralities of the nodes

Detecting the core of a network by the centralities of the nodes

Quasi-three-dimensional hydrodynamics of the corona region of laser irradiation of a slab

Communicability cosine distance: similarity and symmetry in graphs/networks

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