Global Reconstruction of Complex Network Topology via Structured Compressive Sensing

Dai, Jingchao; Huang, Keke; Liu, Yishun; Yang, Chunhua; Wang, Zhen

doi:10.1109/jsyst.2020.2997713

Cited by 17 publications

(4 citation statements)

References 46 publications

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“…In the network topology matrix, the cluster effect is characteristic of the sparsity of its column vector. Therefore, compressed sensing can be used to reconstruct the network topology matrix, and we can accurately reconstruct the complete information of the debt complex network using only underdetermined information 18 . According to the reconstructed debt network, we can accurately analyze the contagion process theory of enterprises’ debt defaults.…”

Section: Reconstruction Model Of Enterprise Debt Networkmentioning

confidence: 99%

Reconstruction of enterprise debt networks based on compressed sensing

Liang

Zhang

et al. 2023

Sci Rep

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This study aims at the problem of reconstruction the unknown links in debt networks among enterprises. We use the topological matrix of the enterprise debt network as the object of reconstruction and use the time series data of accounts receivable and payable as input and output information in the debt network to establish an underdetermined linear system about the topological matrix of the debt network. We establish an iteratively reweighted least-squares algorithm, which is an algorithm in compressed sensing. This algorithm uses reweighted $$\ell _2$$ ℓ 2 -minimization to approximate $$\ell _1$$ ℓ 1 -norm of the target vectors. We solve the $$\ell _1$$ ℓ 1 -minimization problem of the underdetermined linear system using the iteratively reweighted least-squares algorithm and obtain the reconstructed topological matrix of the debt network. Simulation experiments show that the topology matrix reconstruction method of enterprise debt networks based on compressed sensing can reconstruct over 70% of the unknown network links, and the error is controlled within 2%.

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Section: Reconstruction Model Of Enterprise Debt Networkmentioning

confidence: 99%

Reconstruction of enterprise debt networks based on compressed sensing

Liang

Zhang

et al. 2023

Sci Rep

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“…Meanwhile, the topology of a complex network is often highly nonlinear, highly interconnected, and scale-invariant [7,8]. Therefore, the identification of node importance in complex networks holds significant practical and research value.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Node Importance Identification Method for Public Transportation Infrastructure Coupled Networks: A Case Study of Chengdu

Zeng,

Sun,

Zhang

2024

Entropy

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Public transportation infrastructure is a typical, complex, coupled network that is usually composed of connected bus lines and subway networks. This study proposes an entropy-based node importance identification method for this type of coupled network that is helpful for the integrated planning of urban public transport and traffic flows, as well as enhancing network information dissemination and maintaining network resilience. The proposed method develops a systematic entropy-based metric based on five centrality metrics, namely the degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), and clustering coefficient (CCO). It then identifies the most important nodes in the coupled networks by considering the information entropy of the nodes and their neighboring ones. To evaluate the performance of the proposed method, a bus–subway coupled network in Chengdu, containing 10,652 nodes and 15,476 edges, is employed as a case study. Four network resilience assessment metrics, namely the maximum connectivity coefficient (MCC), network efficiency (NE), susceptibility (S), and natural connectivity (NC), were used to conduct group experiments. The experimental results demonstrate the following: (1) the multi-functional fitting analysis improves the analytical accuracy by 30% as compared to fitting with power law functions only; (2) for both CC and CCO, the improved metric’s performance in important node identification is greatly improved, and it demonstrates good network resilience.

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“…Successful free riders may result in vaccination levels lower than the optimal vaccination levels required by the population, implying that a vaccination dilemma arises [14] , [15] . To investigate the vaccination dilemma, evolutionary game theory [16] , [17] and network theory [18] , [19] offer a useful tool for studying this perplexing dilemma. Fu mentioned a two-stage model to describe voluntary vaccination campaigns by combining evolutionary game theory and epidemic processes [20] .…”

Section: Introductionmentioning

confidence: 99%

Effect of vaccine efficacy on disease transmission with age-structured

Ши

2022

Chaos, Solitons & Fractals

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Recent outbreaks of novel infectious diseases (e.g., COVID-19, H2N3) have highlighted the threat of pathogen transmission, and vaccination offers a necessary tool to relieve illness. However, vaccine efficacy is one of the barriers to eradicating the epidemic. Intuitively, vaccine efficacy is closely related to age structures, and the distribution of vaccine efficacy usually obeys a Gaussian distribution, such as with H3N2 and influenza A and B. Based on this fact, in this paper, we study the effect of vaccine efficacy on disease spread by considering different age structures and extending the traditional susceptible-infected-recovery/vaccinator(SIR/V) model with two stages to three stages, which includes the decision-making stage, epidemic stage, and birth-death stage. Extensive numerical simulations show that our model generates a higher vaccination level compared with the case of complete vaccine efficacy because the vaccinated individuals in our model can form small and numerous clusters slower than that of complete vaccine efficacy. In addition, priority vaccination for the elderly is conducive to halting the epidemic when facing population ageing. Our work is expected to provide valuable information for decision-making and the design of more effective disease control strategies.

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Global Reconstruction of Complex Network Topology via Structured Compressive Sensing

Cited by 17 publications

References 46 publications

Reconstruction of enterprise debt networks based on compressed sensing

Reconstruction of enterprise debt networks based on compressed sensing

Entropy-Based Node Importance Identification Method for Public Transportation Infrastructure Coupled Networks: A Case Study of Chengdu

Effect of vaccine efficacy on disease transmission with age-structured

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