Influential nodes identification method based on adaptive adjustment of voting ability

Wang, Guan; Alias, Syazwina Binti; Sun, Zejun; Wang, Feifei; Fan, Aiwan; Hu, Haifeng

doi:10.1016/j.heliyon.2023.e16112

Cited by 5 publications

(1 citation statement)

References 37 publications

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“…The SIR model is used to calculate their infection values and to measure the infection capacity of multiple nodes. For the SIR model, α is set to 0.5, β is set to 1, the infection time t is set to 30, and the number of iterations is set to 1000 55 , 56 . The infection results of each algorithm are shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

Excavating important nodes in complex networks based on the heat conduction model

Hu,

Zheng,

et al. 2024

Sci Rep

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Analyzing the important nodes of complex systems by complex network theory can effectively solve the scientific bottlenecks in various aspects of these systems, and how to excavate important nodes has become a hot topic in complex network research. This paper proposes an algorithm for excavating important nodes based on the heat conduction model (HCM), which measures the importance of nodes by their output capacity. The number and importance of a node’s neighbors are first used to determine its own capacity, its output capacity is then calculated based on the HCM while considering the network density, distance between nodes, and degree density of other nodes. The importance of the node is finally measured by the magnitude of the output capacity. The similarity experiments of node importance, sorting and comparison experiments of important nodes, and capability experiments of multi-node infection are conducted in nine real networks using the Susceptible-Infected-Removed model as the evaluation criteria. Further, capability experiments of multi-node infection are conducted using the Independent cascade model. The effectiveness of the HCM is demonstrated through a comparison with eight other algorithms for excavating important nodes.

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Section: Resultsmentioning

confidence: 99%

Excavating important nodes in complex networks based on the heat conduction model

Hu,

Zheng,

et al. 2024

Sci Rep

Self Cite

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A fusion of neural, genetic and ensemble machine learning approaches for enhancing the engineering predictive capabilities of lightweight foamed reinforced concrete beam

Chen,

Zeng,

Jia

et al. 2024

Powder Technology

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Finding influential nodes in complex networks based on Kullback–Leibler model within the neighborhood

Wang,

Sun,

Wang

et al. 2024

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As a research hot topic in the field of network security, the implementation of machine learning, such as federated learning, involves information interactions among a large number of distributed network devices. If we regard these distributed network devices and connection relationships as a complex network, we can identify the influential nodes to find the crucial points for optimizing the imbalance of the reliability of devices in federated learning system. This paper will analyze the advantages and disadvantages of existing algorithms for identifying influential nodes in complex networks, and propose a method from the perspective of information dissemination for finding influential nodes based on Kullback–Leibler divergence model within the neighborhood (KLN). Firstly, the KLN algorithm removes a node to simulate the scenario of node failure in the information dissemination process. Secondly, KLN evaluates the loss of information entropy within the neighborhood after node removal by establishing the KL divergence model. Finally, it assesses the damage influence of the removed node by integrating the network attributes and KL divergence model, thus achieving the evaluation of node importance. To validate the performance of KLN, this paper conducts an analysis and comparison of its results with those of 11 other algorithms on 10 networks, using SIR model as a reference. Additionally, a case study was undertaken on a real epidemic propagation network, leading to the proposal of management and control strategies for daily protection based on the influential nodes. The experimental results indicate that KLN effectively evaluates the importance of the removed node using KL model within the neighborhood, and demonstrate better accuracy and applicability across networks of different scales.

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Influential nodes identification method based on adaptive adjustment of voting ability

Cited by 5 publications

References 37 publications

Excavating important nodes in complex networks based on the heat conduction model

Excavating important nodes in complex networks based on the heat conduction model

A fusion of neural, genetic and ensemble machine learning approaches for enhancing the engineering predictive capabilities of lightweight foamed reinforced concrete beam

Finding influential nodes in complex networks based on Kullback–Leibler model within the neighborhood

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