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

被引:0
作者
Wang, Guan [1 ]
Sun, Zejun [1 ]
Wang, Tianqin [2 ]
Li, Yuanzhe [3 ]
Hu, Haifeng [1 ]
机构
[1] Pingdingshan Univ, Sch Informat Engn, Pingdingshan 467000, Peoples R China
[2] Puyang Technician Coll, Mech Dept, Puyang 457000, Peoples R China
[3] Baofeng Cty Peoples Govt, Pingdingshan 467000, Peoples R China
关键词
Complex networks; Information dissemination; Influential nodes; Kullback-Leibler divergence; Neighborhood; CENTRALITY; SPREADERS;
D O I
10.1038/s41598-024-64122-4
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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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页数:22
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