基于多指标决策矩阵的超网络节点重要性辨识方法

朱福祺; 贾晓妍; 卫良; 李发旭

doi:10.13306/j.1672-3813.2026.03.004

复杂系统与复杂性科学 >

2026 , Vol. 23 >Issue 3: 27 - 36

DOI: https://doi.org/10.13306/j.1672-3813.2026.03.004

复杂网络

基于多指标决策矩阵的超网络节点重要性辨识方法

朱福祺 ,
贾晓妍 ,
卫良 ,
李发旭

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青海师范大学 a.计算机学院;b.藏语智能信息处理及应用国家重点实验室;c.美术学院, 西宁 810008

朱福祺(1999-),女,河南开封人,硕士,主要研究方向为超网络理论及其应用。

收稿日期: 2024-08-14

修回日期: 2024-09-13

网络出版日期: 2026-07-14

基金资助

国家自然科学基金(61663041);青海省自然科学基金(2023-ZJ-916M)

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Importance Recognition of Nodes in Hypernetworks Based on Multi-indicator Decision Matrix

ZHU Fuqi ,
JIA Xiaoyan ,
WEI Liang ,
LI Faxu

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a. College of Computer; b. The State Key Laboratory of Tibetan Intelligent Information Processing and Application; c. Academy of Fine Arts, Qinghai Normal University, Xining 810008, China

Received date: 2024-08-14

Revised date: 2024-09-13

Online published: 2026-07-14

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摘要

针对超网络中重要节点识别方法忽略了超边对节点的影响和评价指标较为单一的问题,提出了一种基于多指标决策矩阵的节点重要性辨识方法。该方法利用超度表征节点的局部重要性,考虑到超边对节点的影响,定义了矢量子图中心度刻画节点的扩散能力,并通过介数中心性反映节点的位置信息,以此度量节点的全局影响力,最终根据熵理论确定各指标的贡献权重,从节点自身和关联超边的影响评估节点的重要性。通过在不同类型的超网络中进行单调性、鲁棒性以及SIR传播模型的实验验证,结果表明该方法能更准确有效地辨识重要节点。

关键词： 超网络; 节点重要性; 超边影响; 多指标决策矩阵; 矢量子图中心度

本文引用格式

朱福祺 , 贾晓妍 , 卫良 , 李发旭 . 基于多指标决策矩阵的超网络节点重要性辨识方法[J]. 复杂系统与复杂性科学, 2026 , 23(3) : 27 -36 . DOI: 10.13306/j.1672-3813.2026.03.004

Abstract

Aiming at the problems that the methods for recognizing important nodes in hypernetworks ignore the influence of hyperedges on nodes and the evaluation indexes are relatively single, a method of node importance identification based on multi-indicator decision matrix is proposed. The method characterizes the local importance of node using the hyperdegree, considering the effect of hyperedges on nodes, the vector subgraph centrality is defined to portray the diffusion ability of nodes, and measures the global influence of node by reflecting its positional information through betweenness centrality, the contribution weights of each metric are determined based on entropy theory to assess the importance of the node in terms of its own influence and that of the associated hyperedges. Through the experimental validation of monotonicity, robustness, and SIR propagation model in different types of hypernetworks, the results show that the method can recognize the important nodes in the network more accurately and effectively.

Key words： hypernetwork; node importance; hyperedge effect; multi-indicator decision matrix; vector subgraph centrality

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