摘要
为了克服现有关键节点识别技术存在的计算复杂性大、评估维度单一和应用范围有限等缺点,构造了一个适用于关键节点评估的新算法。该算法首先通过分析节点的信息熵以及其邻居节点的影响力贡献,评估节点的局部影响力,从而消除了传统仅仅依赖节点度量为评估标准的瑕疵。其次,该算法通过衡量节点间距离的相关性来确定节点的全局影响力,有效解决了因考虑过多路径而导致的计算量激增的问题。为了充分论证算法的实用性,借助单调性实验、传染病模型实验以及鲁棒性实验,对4个规模各异的真实网络以及6种比较算法展开分析。最终结果显示该算法在准确性、有效性和识别能力等方面均有一定改善,同时,其计算复杂度较低,可应用于稀疏的网络。
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.
作者
张正勇
苏健生
姜敏勤
杨钰
ZHANG Zhengyong;SU Jiansheng;JIANG Minqin;YANG Yu(School of Management Science and Engineering,Nanjing University of Finance and Economics,Nanjing 210023,China)
出处
《南京航空航天大学学报(自然科学版)》
2025年第2期387-396,共10页
Journal of Nanjing University of Aeronautics & Astronautics
基金
国家自然科学基金(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)。
关键词
复杂网络
关键节点
节点信息熵
全局信息
局部信息
complex networks
key nodes
node information entropy
global information
local information
作者简介
通信作者:张正勇,男,副教授,E-mail:zyzhang@nufe.edu.cn。
