通信学报 ›› 2021, Vol. 42 ›› Issue (6): 72-83.doi: 10.11959/j.issn.1000-436x.2021088

• 学术论文 • 上一篇    

基于改进灰狼优化的复杂网络重要节点识别算法

顾秋阳1,2, 吴宝1,2, 孙兆洋3, 池仁勇1,2   

  1. 1 浙江工业大学管理学院,浙江 杭州 310023
    2 浙江工业大学中国中小企业研究院,浙江 杭州 310023
    3 中国标准化研究院高新技术标准化研究所,北京 100191
  • 修回日期:2021-03-24 出版日期:2021-06-01 发布日期:2021-06-01
  • 作者简介:顾秋阳(1995− ),男,浙江杭州人,浙江工业大学博士生,主要研究方向为智能信息处理、数据挖掘、中小企业高质量发展等
    吴宝(1979− ),男,浙江金华人,博士,浙江工业大学研究员、博士生导师,主要研究方向为复杂网络链路预测、金融信用风险控制与中小企业发展
    孙兆洋(1979− ),女,北京人,博士,中国标准化研究院副研究员,主要研究方向为智能信息处理与数据挖掘
    池仁勇(1959− ),男,浙江温州人,博士,浙江工业大学教授、博士生导师,主要研究方向为复杂网络链路预测、中小企业智能信息管理与创新创业
  • 基金资助:
    国家自然科学基金资助项目(71571162);国家自然科学基金资助项目(71772164);浙江省哲学社会科学重大课题基金资助项目(20YSXK02ZD)

Key node identification algorithm for complex network based on improved grey wolf optimization

Qiuyang GU1,2, Bao WU1,2, Zhaoyang SUN3, Renyong CHI1,2   

  1. 1 School of Management, Zhejiang University of Technology, Hangzhou 310023, China
    2 China Institute for Small and Medium Enterprises, Zhejiang University of Technology, Hangzhou 310023, China
    3 Institute of High and New Technology Standardization, China Institute of Standardization, Beijing 100191, China
  • Revised:2021-03-24 Online:2021-06-01 Published:2021-06-01
  • Supported by:
    The National Natural Science Foundation of China(71571162);The National Natural Science Foundation of China(71772164);The Philosophy and Social Science Major Project of Zhejiang Province(20YSXK02ZD)

摘要:

近年来,如何识别影响力最大的重要节点已成为网络科学最前沿的热点方向。将复杂网络节点影响力最大化问题表述为一个优化问题,其成本函数表示为节点影响力及其间的距离,使用Shannon熵对节点影响力进行度量,并利用一种改进灰狼优化算法来解决此问题。最后,使用真实复杂网络数据集进行数值计算。结果表明,与现有算法相比,所提算法精度更高,且计算效率较高。

关键词: 改进灰狼优化, 复杂网络, 重要节点识别, 影响力最大化

Abstract:

In recent years, how to select the most influential key node for identification has become the most cutting-edge hot direction in network science.Formulating the problem of maximizing the influence of complex network nodes as an optimization problem whose cost function was expressed as the influence of nodes and the distance between them, measures user influence using Shannon entropy, and solved this problem using an improved gray wolf optimization algorithm.Finally, numerical examples were performed with real complex network datasets.The experimental results show that the proposed algorithm is more accurate and computationally efficient than the existing method.

Key words: improved grey wolf optimization, complex network, key node identification, the maximization of influence

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