基于复杂网络多阶邻域贡献度的节点重要性序结构辨识

胡钢; 卢志宇; 王乐萌; 许丽鹏; 徐翔; 任勇军

doi:10.12263/DZXB.20221109

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基于复杂网络多阶邻域贡献度的节点重要性序结构辨识

学术论文 | 更新时间：2025-12-08

- 基于复杂网络多阶邻域贡献度的节点重要性序结构辨识
- Identification of Node Importance Order Structure Based on Multi-Order Neighborhood Contribution of Complex Network
- 电子学报 2023年51卷第7期页码：1956-1963
- 作者机构：
  
  1.安徽工业大学管理科学与工程学院,安徽马鞍山 243032
  2.东南大学经济管理学院,江苏南京 211189
  3.国防科技大学信息系统工程重点实验室,湖南长沙 410073
  4.南京信息工程大学计算机与软件学院,江苏南京 210044
- 作者简介：
  
  [ "胡钢男，1970年出生于甘肃省天水市，现为安徽工业大学管理科学与工程学院副教授，硕士生导师.研究方向：多属性决策、复杂网络系统建模与网络均衡计算. E-mail: hug_2004@126.com" ]
  [ "卢志宇男，1998年出生于安徽省亳州市，现为安徽工业大学管理科学与工程学院在读硕士研究生.研究方向：复杂网络建模与分析. E-mail: lzy_910@163.com" ]
- 基金信息：
  
  国家自然科学基金项目(71772002);安徽省自然科学基金项目(2108085MG236);安徽省高校自然科学研究项目(KJ2021A0385)
- DOI：10.12263/DZXB.20221109
  中图分类号： TP393
- 收稿：2022-09-30，
  
  修回：2023-05-24，
  
  纸质出版：2023-07-25
- 稿件说明：
移动端阅览
胡钢,卢志宇,王乐萌等.基于复杂网络多阶邻域贡献度的节点重要性序结构辨识[J].电子学报,2023,51(07):1956-1963.

HU Gang,LU Zhi-yu,WANG Le-meng,et al.Identification of Node Importance Order Structure Based on Multi-Order Neighborhood Contribution of Complex Network[J].ACTA ELECTRONICA SINICA,2023,51(07):1956-1963.
胡钢,卢志宇,王乐萌等.基于复杂网络多阶邻域贡献度的节点重要性序结构辨识[J].电子学报,2023,51(07):1956-1963. DOI： 10.12263/DZXB.20221109.

HU Gang,LU Zhi-yu,WANG Le-meng,et al.Identification of Node Importance Order Structure Based on Multi-Order Neighborhood Contribution of Complex Network[J].ACTA ELECTRONICA SINICA,2023,51(07):1956-1963. DOI： 10.12263/DZXB.20221109.

摘要

为更精细化辨识节点重要性，本文研究节点多阶交互演化对节点重要性序结构形成的影响，提出基于复杂网络多阶邻域贡献度的节点重要性序结构辨识系统模型.首先，基于节点间不同阶层交互关系和节点多阶邻域规模异质性程度构建多阶邻域贡献度模型；通过节点多阶圈信息集结节点自身多阶邻域空间结构信息；其次，构建融合多阶邻域空间位置信息贡献和多阶圈信息贡献的节点重要性辨识系统模型，给出节点序结构辨识算法；最后，仿真分析表明在各真实网络中本文算法相比经典算法最高提升88%节点辨识率，以0.5资源投入进行网络攻击，分别最大提升67.47%，39.40%和20.17%攻击效用值.

Abstract

In order to finely identify the node importance

this paper studies the influence of node multi-order interaction evolution on node importance formation

and proposes a system model of node importance sequence structure identification based on multi-order neighborhood contribution of complex networks. Firstly

a multi-order neighborhood contribution model is constructed based on different hierarchical interactions between nodes and the degree of heterogeneity of nodes' multi-order neighborhood scale; and characterize the nodes' own multi-order neighborhood spatial structure information by nodes' multi-order circle information. Secondly

a node importance identification model is constructed by integrating the contributions of multi-order neighborhood spatial location information and multi-order circle information

and a node order structure identification algorithm is given. Finally

the simulation analysis shows that this algorithm improves the node identification rate by up to 88% compared with the classical algorithm in each real network

and improves the attack utility value by up to 67.47%

39.40% and 20.17% with 0.5 resource input for network attacks

respectively.

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