基于网络跨层信息熵的复杂网络节点重要性辨识  

Node Importance Identification in Complex Networks Based on Multi-layer Iterative Information Entropy

作　　者：胡钢[1,2] 王琴 HU Gang;WANG Qin(School of Management Science and Engineering,Anhui University of Technology,Maanshan 243032 China;Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes,Maanshan 243032 China)

机构地区：[1]安徽工业大学管理科学与工程学院,安徽马鞍山243032 [2]复杂系统多学科管理与控制安徽普通高校重点实验室,安徽马鞍山243032

出　　处：《西华大学学报(自然科学版)》2025年第2期70-78,共9页Journal of Xihua University:Natural Science Edition

基　　金：国家社会科学基金项目(19GBL254);安徽省自然科学基金项目(2108085MC236);安徽省高校自然科学研究项目(KJ2021A0385);安徽普通高校重点实验室开放基金项目(CS2021-05)。

摘　　要：为解决经典K-shell分解算法范式化对复杂网络分层分级,导致网络层间层内节点辨识精细度降低等问题,提出一种网络跨层邻度(信息)熵算法。该算法首先改进K-shell分解算法分层过程,采用网络跨层中心性与网络跨层中心度以细化网络节点位置重要性;其次,综合分析网络跨层中心度、邻域中心性与信息熵中所包含的节点位置信息与邻居信息,采用网络跨层邻度熵算法对网络节点重要性进行辨识;最后,基于不同拓扑结构的5种网络,与其他算法分别就单调性、准确性及时间性能进行比较实验,实验结果表明,网络跨层邻度熵算法单调性最高可达0.9999,精确性比其他算法最高提升21%。该算法具有更优越的网络节点辨识能力。In order to solve the problem of reducing the precision of node identification between layers and within layers due to the hierarchical classification of complex networks by the classical K-shell decomposition algorithm,the network cross-layer adjacency entropy(multi-layer iterative information entropy)algorithm was proposed.Firstly,the decomposition process of K-shell decomposition algorithm was improved,and cross-layer centrality and cross-layer center degree of network were proposed to refine the importance of network node location.Secondly,the node location information and neighbor information contained in the network cross-layer centrality,neighborhood centrality and information entropy were analyzed comprehensively,and the network cross-layer adjacency entropy algorithm was proposed to identify the importance of network nodes.Finally,five kinds of networks with different topologies were compared with other algorithms in terms of monotonicity,accuracy and time performance.The experimental results show that the monotonicity of the cross-layer adjacency entropy algorithm is up to 0.9999,and the accuracy is up to 21%higher than other algorithms,which indicates that the proposed algorithm has better ability to identify network nodes.

关 键 词：K核分解 网络跨层中心性 网络跨层邻度熵 节点重要性 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]