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复杂系统与复杂性科学  2025, Vol. 22 Issue (4): 24-28    DOI: 10.13306/j.1672-3813.2025.04.004
  复杂网络 本期目录 | 过刊浏览 | 高级检索 |
基于图卷积网络的复杂网络能控性提升方法
卢新彪, 刘泽诚, 陈贵允, 杨铁流, 高兴
河海大学人工智能与自动化学院,南京 211100
Improving Network Controllability: a Graph Convolutional Network Based Approach
LU Xinbiao, LIU Zecheng, CHEN Guiyun, YANG Tieliu, GAO Xing
School of Artificial Intelligence and Automation, Hohai University, Nanjing 211100, China
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摘要 为提升网络能控性提出了一种基于图卷积神经网络的复杂网络能控性提升方法,该方法首先训练一个图卷积网络用于选择合适的节点,然后在这些被选择的节点之间随机增加连边。在两类典型复杂网络模型上的数值仿真表明,与传统的在所有节点之间随机增加连边的方法相比,该方法大大减少了使网络达到能控状态所需增加连边的数量,提高了增强网络能控性的效率。
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卢新彪
刘泽诚
陈贵允
杨铁流
高兴
关键词 复杂网络能控性图卷积网络预测    
Abstract:In order to improve network controllability, a network controllability improvement method based on graph convolutional neural network is proposed, in which a graph convolutional network is first trained to select appropriate nodes, and then edges are randomly added between these selected nodes. Numerical simulations are carried out on two representative complex network models. Compared with the traditional method in which edges are added randomly between all nodes, the proposed method greatly reduces the number of added edges, which is more efficient.
Key wordscomplex network    controllability    graph convolutional network    prediction
收稿日期: 2024-01-02      出版日期: 2025-12-10
ZTFLH:  N94  
  TP183  
基金资助:国家自然科学基金(61573001)
作者简介: 卢新彪(1975),男,山东曹县人,博士,副教授,主要研究方向为多智能体群体智能分析与控制,复杂动态网络同步控制等。
引用本文:   
卢新彪, 刘泽诚, 陈贵允, 杨铁流, 高兴. 基于图卷积网络的复杂网络能控性提升方法[J]. 复杂系统与复杂性科学, 2025, 22(4): 24-28.
LU Xinbiao, LIU Zecheng, CHEN Guiyun, YANG Tieliu, GAO Xing. Improving Network Controllability: a Graph Convolutional Network Based Approach[J]. Complex Systems and Complexity Science, 2025, 22(4): 24-28.
链接本文:  
https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2025.04.004      或      https://fzkx.qdu.edu.cn/CN/Y2025/V22/I4/24
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