通信学报 ›› 2021, Vol. 42 ›› Issue (6): 72-83.doi: 10.11959/j.issn.1000-436x.2021088
顾秋阳1,2, 吴宝1,2, 孙兆洋3, 池仁勇1,2
修回日期:
2021-03-24
出版日期:
2021-06-25
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基金资助:
Qiuyang GU1,2, Bao WU1,2, Zhaoyang SUN3, Renyong CHI1,2
Revised:
2021-03-24
Online:
2021-06-25
Published:
2021-06-01
Supported by:
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。
中图分类号:
顾秋阳, 吴宝, 孙兆洋, 池仁勇. 基于改进灰狼优化的复杂网络重要节点识别算法[J]. 通信学报, 2021, 42(6): 72-83.
Qiuyang GU, Bao WU, Zhaoyang SUN, Renyong CHI. Key node identification algorithm for complex network based on improved grey wolf optimization[J]. Journal on Communications, 2021, 42(6): 72-83.
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