基于改进灰狼优化的复杂网络重要节点识别算法

顾秋阳; 吴宝; 孙兆洋; 池仁勇

doi:10.11959/j.issn.1000-436x.2021088

您当前的位置：

首页 >

文章列表页 >

基于改进灰狼优化的复杂网络重要节点识别算法

学术论文 | 更新时间：2024-06-05

- 基于改进灰狼优化的复杂网络重要节点识别算法
- Key node identification algorithm for complex network based on improved grey wolf optimization
- 通信学报 2021年42卷第6期页码：72-83
- 作者机构：
  
  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
- 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" ]
- 基金信息：
  
  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)
- DOI：10.11959/j.issn.1000-436x.2021088
  中图分类号： TP181，TP391
- 网络出版日期：2021-06，
  
  纸质出版日期：2021-06-25
- 稿件说明：
移动端阅览
顾秋阳, 吴宝, 孙兆洋, 等. 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, et al. Key node identification algorithm for complex network based on improved grey wolf optimization[J]. Journal on communications, 2021, 42(6): 72-83.
顾秋阳, 吴宝, 孙兆洋, 等. 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. DOI： 10.11959/j.issn.1000-436x.2021088.

Qiuyang GU, Bao WU, Zhaoyang SUN, et al. Key node identification algorithm for complex network based on improved grey wolf optimization[J]. Journal on communications, 2021, 42(6): 72-83. DOI： 10.11959/j.issn.1000-436x.2021088.

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。

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.

关键词

Keywords

references

KIMURA M , SAITO K , NAKANO R , et al . Extracting influential nodes on a social network for information diffusion [J ] . Data Mining and Knowledge Discovery , 2009 , 20 ( 1 ): 70 - 97 .

SHEIKHAHMADI A , NEMATBAKHSH M A , ZAREIE A . Identification of influential users by neighbors in online social networks [J ] . Physica A:Statistical Mechanics and Its Applications , 2017 , 486 : 517 - 534 .

邓琨 , 李文平 , 余法红 , 等 . 基于多核心标签传播的复杂网络重叠社区识别方法 [J ] . 通信学报 , 2017 , 38 ( 2 ): 53 - 66 .

DENG K , LI W P , YU F H , et al . Overlapping community detection in complex networks based on multi kernel label propagation [J ] . Journal on Communications , 2017 , 38 ( 2 ): 53 - 66 .

ZAREIE A , SHEIKHAHMADI A , JALILI M . Identification of influential users in social networks based on users’ interest [J ] . Information Sciences , 2019 , 493 : 217 - 231 .

ZAREIE A , SHEIKHAHMADI A , KHAMFOROOSH K . Influence maximization in social networks based on TOPSIS [J ] . Expert Systems With Applications , 2018 , 108 : 96 - 107 .

LU F , ZHANG W K , SHAO L W , et al . Scalable influence maximization under independent cascade model [J ] . Journal of Network and Computer Applications , 2017 , 86 : 15 - 23 .

SHEIKHAHMADI A , NEMATBAKHSH M A , SHOKROLLAHI A . Improving detection of influential nodes in complex networks [J ] . Physica A:Statistical Mechanics and Its Applications , 2015 , 436 : 833 - 845 .

刘露 , 胡封晔 , 牛亮 , 等 . 异质网络中基于节点影响力的相似度度量方法 [J ] . 电子学报 , 2019 , 47 ( 9 ): 1929 - 1936 .

LIU L , HU F Y , NIU L , et al . Node influence based similarity measure method in heterogeneous network [J ] . Acta Electronica Sinica , 2019 , 47 ( 9 ): 1929 - 1936 .

ZAREIE A , SHEIKHAHMADI A , JALILI M . Influential node ranking in social networks based on neighborhood diversity [J ] . Future Generation Computer Systems , 2019 , 94 : 120 - 129 .

韩忠明 , 陈炎 , 李梦琪 , 等 . 一种有效的基于三角结构的复杂网络节点影响力度量模型 [J ] . 物理学报 , 2016 , 65 ( 16 ): 289 - 300 .

HAN Z M , CHEN Y , LI M Q , et al . An efficient node influence metric based on triangle in complex networks [J ] . Acta Physica Sinica , 2016 , 65 ( 16 ): 289 - 300 .

HUANG C Y , LEE C L , WEN T H , et al . A computer virus spreading model based on resource limitations and interaction costs [J ] . Journal of Systems and Software , 2013 , 86 ( 3 ): 801 - 808 .

JALILI M , PERC M . Information cascades in complex networks [J ] . Journal of Complex Networks , 2017 , 5 ( 5 ): 665 - 693 .

NOWZARI C , PRECIADO V M , PAPPAS G J . Analysis and control of epidemics:a survey of spreading processes on complex networks [J ] . IEEE Control Systems Magazine , 2016 , 36 ( 1 ): 26 - 46 .

韩忠明 , 陈炎 , 刘雯 , 等 . 社会网络节点影响力分析研究 [J ] . 软件学报 , 2017 , 28 ( 1 ): 84 - 104 .

HAN Z M , CHEN Y , LIU W , et al . Research on node influence analysis in social networks [J ] . Journal of Software , 2017 , 28 ( 1 ): 84 - 104 .

FREEMAN L C . Centrality in social networks conceptual clarification [J ] . Social Networks , 1978 , 1 ( 3 ): 215 - 239 .

FREEMAN L C . A set of measures of centrality based on betweenness [J ] . Sociometry , 1977 , 40 ( 1 ): 35 .

SABIDUSSI G . The centrality of a graph [J ] . Psychometrika , 1966 , 31 ( 4 ): 581 - 603 .

KITSAK M , GALLOS L K , HAVLIN S , et al . Identification of influential spreaders in complex networks [J ] . Nature Physics , 2010 , 6 ( 11 ): 888 - 893 .

于冬梅 , 高雷阜 , 赵世杰 . 考虑凸形障碍的应急设施选址与资源分配决策研究 [J ] . 系统工程理论与实践 , 2019 , 39 ( 5 ): 1178 - 1188 .

YU D M , GAO L F , ZHAO S J . Emergency facility location-allocation problem with convex barriers [J ] . Systems Engineering-Theory ＆Practice , 2019 , 39 ( 5 ): 1178 - 1188 .

WANG Y , CONG G , SONG G J , et al . Community-based greedy algorithm for mining top-K influential nodes in mobile social networks [C ] // Proceedings of the 16th ACM SIGKDD International Conference on Knowledge discovery and Data Mining . New York:ACM Press , 2010 : 1039 - 1048 .

GONG M G , YAN J N , SHEN B , et al . Influence maximization in social networks based on discrete particle swarm optimization [J ] . Information Sciences , 2016 , 367/368 ( 5 ): 600 - 614 .

BAO Z K , LIU J G , ZHANG H F . Identifying multiple influential spreaders by a heuristic clustering algorithm [J ] . Physics Letters A , 2017 , 381 ( 11 ): 976 - 983 .

CUI L Z , HU H X , YU S , et al . DDSE:a novel evolutionary algorithm based on degree-descending search strategy for influence maximization in social networks [J ] . Journal of Network and Computer Applications , 2018 , 103 : 119 - 130 .

MIRJALILI S , MIRJALILI S M , LEWIS A . Grey wolf optimizer [J ] . Advances in Engineering Software , 2014 , 69 : 46 - 61 .

KEMPE D , KLEINBERG J , TARDOS É . Maximizing the spread of influence through a social network [C ] // Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM Press , 2003 : 137 - 146 .

GOLDENBERG J , LIBAI B , MULLER E . Using complex systems analysis to advance marketing theory development:modeling heterogeneity effects on new product growth through stochastic cellular automata,academy of marketing science review 9 [J ] . Monthly Labor Review , 2001 , 31 ( 3 ): 8 - 11 .

PENG S C , YU S , YANG A M . Smartphone malware and its propagation modeling:a survey [J ] . IEEE Communications Surveys ＆ Tutorials , 2014 , 16 ( 2 ): 925 - 941 .

李理 , 单而芳 . 图上博弈的Page-Shapley值 [J ] . 系统工程理论与实践 , 2019 , 39 ( 11 ): 2771 - 2783 .

LI L , SHAN E F . The Page-Shapley values for graph games [J ] . Systems Engineering-Theory ＆ Practice , 2019 , 39 ( 11 ): 2771 - 2783 .

CHEN W , WANG Y J , YANG S Y . Efficient influence maximization in social networks [C ] // Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM Press , 2009 : 1 - 9 .

WANG X J , SU Y Y , ZHAO C L , et al . Effective identification of multiple influential spreaders by DegreePunishment [J ] . Physica A:Statistical Mechanics and Its Applications , 2016 , 461 : 238 - 247 .

GUO L , LIN J H , GUO Q , et al . Identifying multiple influential spreaders in term of the distance-based coloring [J ] . Physics Letters A , 2016 , 380 ( 7/8 ): 837 - 842 .

JIANG Q , SONG G , CONG G , et al . Simulated annealing based influence maximization in social networks [C ] // Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence . Palo Alto:AAAI Press , 2011 : 127 - 132 .

SUN Y F . Optimal selection of nodes to propagate influence on networks [J ] . The European Physical Journal B , 2016 , 89 ( 11 ): 253 .

BORRERO J S , PROKOPYEV O A , KROKHMAL P . Optimization of cascading processes in arbitrary networks with stochastic interactions [J ] . IEEE Transactions on Network Science and Engineering , 2019 , 6 ( 4 ): 773 - 787 .

於志勇 , 陈基杰 , 郭昆 , 等 . 基于影响力与种子扩展的重叠社区发现 [J ] . 电子学报 , 2019 , 47 ( 1 ): 153 - 160 .

YU Z Y , CHEN J J , GUO K , et al . Overlapping community detection based on influence and seeds extension [J ] . Acta Electronica Sinica , 2019 , 47 ( 1 ): 153 - 160 .

王炯滔 , 金明 , 李有明 , 等 . 基于Friedman检验的非参数协作频谱感知方法 [J ] . 电子与信息学报 , 2014 , 36 ( 1 ): 61 - 66 .

WANG J T , JIN M , LI Y M , et al . Nonparametric cooperative spectrum sensing algorithm based on Friedman test [J ] . Journal of Electronics ＆ Information Technology , 2014 , 36 ( 1 ): 61 - 66 .

浏览量

440

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于动态图嵌入的车联网拓扑控制

基于高阶路径相似度的复杂网络链路预测方法

基于无标度网络的车联网连通性研究

基于分形特性的复杂网络全局效率估计方法

基于资源传输匹配度的复杂网络链路预测方法