西华大学学报（自然科学版）2025，Vol.44Issue(2)：70-78,9.DOI:10.12198/j.issn.1673-159X.5325
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
摘要
Abstract
In order to solve the problem of reducing the precision of node identification between lay-ers and within layers due to the hierarchical classification of complex networks by the classical K-shell de-composition algorithm,the network cross-layer adjacency entropy(multi-layer iterative information en-tropy)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 ana-lyzed 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 accur-acy is up to 21%higher than other algorithms,which indicates that the proposed algorithm has better abil-ity 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Key words
K-shell decomposition/network cross-layer centrality/network cross-layer adjacency entropy/node importance分类
计算机与自动化引用本文复制引用
胡钢,王琴..基于网络跨层信息熵的复杂网络节点重要性辨识[J].西华大学学报（自然科学版）,2025,44(2):70-78,9.基金项目
国家社会科学基金项目(19GBL254) （19GBL254）
安徽省自然科学基金项目(2108085MC236) （2108085MC236）
安徽省高校自然科学研究项目(KJ2021 A0385) （KJ2021 A0385）
安徽普通高校重点实验室开放基金项目(CS2021-05). （CS2021-05）
