摘要
针对超网络中重要节点识别方法分辨率不足、识别结果不够具体和全面的问题,结合节点度、超度、邻接度和邻接超度利用信息熵公式提出识别超网络重要节点的复合信息熵。该方法设置了可动态调整的影响系数,通过分析节点度、邻接度、节点超度和邻接超度的影响程度,得到每个节点的复合信息熵。其优势在于考虑了节点和邻接节点的影响,且只利用节点的局部属性,致其复杂度较低。仿真实验部分在科研合作超网络和昆明普线公交线路超网络中进行验证。实验结果表明,该方法能有效识别超网络中的重要节点。
In order to solve the problem of low resolution and lack of concrete and comprehensive recognition results of important nodes in hypernetworks,in this paper,combined with the degree of node,degree of transcendence,degree of adjacency and degree of adjacency,the compound information entropy is proposed to identify the important nodes of the hypernetwork.In this method,the influence coefficient is set,and the composite structure entropy of each node is obtained by analyzing the influence degree of degree of node,degree of adjacency,degree of node transcendence and degree of adjacency.Its advantage is that the influence of nodes and adjacent nodes is considered,and only the local attributes of nodes are used,resulting in lower complexity.The simulation experiments are carried out in the research cooperation hypernetwork and Kunming common bus line hypernetwork.Experimental results show that the proposed method can effectively identify the important nodes in the hypernetwork.
作者
涂贵宇
潘文林
张天军
TU Guiyu;PAN Wenlin;ZHANG Tianjun(School of Mathematics and Computer Science,Yunnan University for Nationalities,Kunming 650504,China;Institute of Software Engineering,Yunnan University for Nationalities,Kunming 650504,China)
出处
《复杂系统与复杂性科学》
北大核心
2025年第1期18-25,共8页
Complex Systems and Complexity Science
关键词
超图
超网络
度
超度
熵
重要节点
hypergraph
hypernetwork
degree
hyper-degree
entropy
important node
作者简介
第一作者:涂贵宇(2002-),女,云南玉溪人,硕士研究生,主要研究方向为智能计算;通信作者:潘文林(1972-),男,怒江泸水人,博士,教授,主要研究方向为智能计算。
