A new way to improve the robustness of complex communication networks by allocating redundancy links

Shi, Chunhui; Peng, Yunfeng; Zhuo, Yue; Tang, Jieying; Long, Keping

doi:10.1088/0031-8949/85/03/035803

Cited by 12 publications

(5 citation statements)

References 11 publications

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“…In the aspect of network connectivity enhancement, Chen et al [2] proposed edge adding strategies based on degree centrality, proximity centrality, intermediate centrality and feature vector centrality respectively to deal with network attacks; Jiang et al [3] changed the network topology by randomly adding edges, low degree adding edges and low intermediate adding edges to enhance the network performance; Cao et al [4] comprehensively studied random edge adding strategy, high dielectric edge connecting strategy and low polarization edge connecting strategy, and considered the cost of network connection; Ghosh et al [5] proposed an edge adding method based on greedy strategy. The objective function is to maximize the second largest Laplacian eigenvalue, and each edge adding selects the edge that can maximize the eigenvalue; Kim et al [6] considered the second small eigenvalue of Laplace matrix and designed a more concise network edge adding strategy based on dichotomy; Shi et al [7] proposed a strategy to protect key nodes, adding redundant edges between key nodes of the network to improve the robustness of scale-free networks; Zhao et al proposed the random local reconnection method, which is used when the network reconnection requires edge addition.…”

Section: Research On Communication Network Topologymentioning

confidence: 99%

Survey on Application of Communication Network Topology Simulation Technology

Zhu,

Zeng,

Gong

et al. 2023

HSET

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As an important part of the study of communication networks, network topology clearly reflects many structural features of communication networks from the physical level and is a major research hotspot in this field. Simulation techniques are becoming more and more widely used in the study of communication networks as computer technology advances and becomes more convenient and intuitive. This paper collates the current research results on the topology of communication networks and the effectiveness of the applications of simulation technology, reviews the current status of research in related fields at home and abroad, and summarises the application of simulation technology in topology research to provide some reference for subsequent researchers in related fields.

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Section: Research On Communication Network Topologymentioning

confidence: 99%

Survey on Application of Communication Network Topology Simulation Technology

Zhu,

Zeng,

Gong

et al. 2023

HSET

View full text Add to dashboard Cite

show abstract

“…In the experiment of random attack or intentional attack on scale-free network, Zhang et al (2011) designed a nonlinear integer programming scheme to improve the robustness of intermediate link failure in the network. When assigning redundant links to complex communication networks, some researchers have constructed a key node protection strategy, that is, assigning some redundant links between key nodes will significantly improve the robustness of scale-free network (Shi et al, 2012).…”

Section: Related Workmentioning

confidence: 99%

Research on innovation features and optimization strategies of industrial clusters from the perspective of TLCN

Luo

Zheng

2022

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PurposeThe focus of industrial cluster innovation lies in the cooperation between enterprises and universities/scientific research institutes to make a theoretical breakthrough in the system and mechanism of industrial cluster network. Under the theoretical framework of cluster network, industrial structure can be optimized and upgraded, and enterprise benefit can be improved. Facing the increasing proliferation and multi-structured enterprise data, how to obtain potential and high-quality innovation features will determine the ability of industrial cluster network innovation, as well as the paper aims to discuss these issues.Design/methodology/approachBased on complex network theory and machine learning method, this paper constructs the structure of “three-layer coupling network” (TLCN), predicts the innovation features of industrial clusters and focuses on the theoretical basis of industrial cluster network innovation model. This paper comprehensively uses intelligent information processing technologies such as network parameters and neural network to predict and analyze the industrial cluster data.FindingsFrom the analysis of the experimental results, the authors obtain five innovative features (policy strength, cooperation, research and development investment, centrality and geographical position) that help to improve the ability of industrial clusters, and give corresponding optimization strategy suggestions according to the result analysis.Originality/valueBuilding a TLCN structure of industrial clusters. Exploring the innovation features of industrial clusters. Establishing the analysis paradigm of machine learning method to predict the innovation features of industrial clusters.

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“…An end-to-end gene regulatory graph neural network method is proposed under the framework of supervised and semi-supervised. Xiao et al [29] proposed an attention-based convolutional neural network link prediction method that can effectively improve the performance by fusing and mining structural features and text features.…”

Section: Literature Reviewmentioning

confidence: 99%

A Link Prediction Method Based on Neural Networks

Yan

2021

Applied Sciences

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Link prediction to optimize network performance is of great significance in network evolution. Because of the complexity of network systems and the uncertainty of network evolution, it faces many challenges. This paper proposes a new link prediction method based on neural networks trained on scale-free networks as input data, and optimized networks trained by link prediction models as output data. In order to solve the influence of the generalization of the neural network on the experiments, a greedy link pruning strategy is applied. We consider network efficiency and the proposed global network structure reliability as objectives to comprehensively evaluate link prediction performance and the advantages of the neural network method. The experimental results demonstrate that the neural network method generates the optimized networks with better network efficiency and global network structure reliability than the traditional link prediction models.

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A new way to improve the robustness of complex communication networks by allocating redundancy links

Cited by 12 publications

References 11 publications

Survey on Application of Communication Network Topology Simulation Technology

Survey on Application of Communication Network Topology Simulation Technology

Research on innovation features and optimization strategies of industrial clusters from the perspective of TLCN

A Link Prediction Method Based on Neural Networks

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