南京航空航天大学学报2025，Vol.57Issue(2)：387-396,10.DOI:10.16356/j.1005-2615.2025.02.020
基于邻域信息熵与有效距离的网络节点识别
Network Node Recognition Based on Neighborhood Information Entropy and Effective Distance
摘要
Abstract
In order to overcome the shortcomings of the existing key node recognition technologies,such as high computational complexity,single evaluation dimension,and limited application scope,this paper constructs a novel algorithm suitable for key node evaluation.This algorithm first evaluates the local influence of a node by analyzing its information entropy and the influence contribution of its neighboring nodes,thereby eliminating the shortcomings of traditional evaluation criteria that rely solely on node metrics.Secondly,this algorithm determines the global influence of nodes by measuring the correlation of distances between them,effectively solving the problem of excessive computation caused by considering multiple paths.In order to fully demonstrate the practicality of the algorithm,four real networks of different scales and six comparative algorithms are analyzed using monotonicity experiments,infectious disease model experiments,and robustness experiments.The final results show that the algorithm has certain improvements in accuracy,effectiveness,and recognition ability.At the same time,its computational complexity is low and it can be applied to sparse networks.关键词
复杂网络/关键节点/节点信息熵/全局信息/局部信息Key words
complex networks/key nodes/node information entropy/global information/local information分类
信息技术与安全科学引用本文复制引用
张正勇,苏健生,姜敏勤,杨钰..基于邻域信息熵与有效距离的网络节点识别[J].南京航空航天大学学报,2025,57(2):387-396,10.基金项目
国家自然科学基金(61602217) （61602217）
江苏高校"青蓝工程" （）
江苏省研究生科研与实践创新计划(KYCX23_1794). （KYCX23_1794）
