Mean First-Passage Time on Scale-Free Networks Based on Rectangle Operation

Wang, Xiaomin; Su, Jing; Ma, Fei; Yao, Bing

doi:10.3389/fphy.2021.675833

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…Uncovering a network's structure has many potential applications so that we can assess the system's resilience [37][38][39], understand the dynamical mechanisms [40], identify significant nodes in a network [41,42], detect community structure [43], locate diffusion sources Hu et al [44,45], and analyze the networks' properties [46][47][48]. In this paper, an Element Elimination Method (EEM) [25] is employed to reconstruct the structure of networks.…”

Section: The Procedures Of the Network Reconstructionmentioning

confidence: 99%

Network Reconstruction in Terms of the Priori Structure Information

Guo

Yang

et al. 2021

Front. Phys.

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In this paper, we investigate the reconstruction of networks based on priori structure information by the Element Elimination Method (EEM). We firstly generate four types of synthetic networks as small-world networks, random networks, regular networks and Apollonian networks. Then, we randomly delete a fraction of links in the original networks. Finally, we employ EEM, the resource allocation (RA) and the structural perturbation method (SPM) to reconstruct four types of synthetic networks with 90% priori structure information. The experimental results show that, comparing with RA and SPM, EEM has higher indices of reconstruction accuracy on four types of synthetic networks. We also compare the reconstruction performance of EEM with RA and SPM on four empirical networks. Higher reconstruction accuracy, measured by local indices of success rates, could be achieved by EEM, which are improved by 64.11 and 47.81%, respectively.

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Section: The Procedures Of the Network Reconstructionmentioning

confidence: 99%

Network Reconstruction in Terms of the Priori Structure Information

Guo

Yang

et al. 2021

Front. Phys.

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First-passage properties of bundled networks

Yuan,

Peng,

Gao

et al. 2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Bundled networks, obtained by attaching a copy of a fiber structure to each node on the base structure, serve as important realistic models for the geometry and dynamics of nontranslationally invariant systems in condensed matter physics. Here, we analyze the first-passage properties, including the mean first-passage time, the mean-trapping time, the global-mean first-passage time (GFPT), and the stationary distribution, of a biased random walk within such networks, in which a random walker moves to a neighbor on base with probability γ and to a neighbor on fiber with probability 1−γ when the walker at a node on base. We reveal the primary properties of both the base and fiber structure, which govern the first-passage characteristics of the bundled network. Explicit expressions between these quantities in the bundled networks and the related quantities in the component structures are presented. GFPT serves as a crucial indicator for evaluating network transport efficiency. Unexpectedly, bases and fibers with similar scaling of GFPT can construct bundled networks exhibiting different scaling behaviors of GFPT. Therefore, bundled networks can be tailored to accommodate specific dynamic property requirements by choosing a suitable base and fiber structure. These findings contribute to advancing the design and optimization of network structures.

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