通信学报 ›› 2020, Vol. 41 ›› Issue (12): 21-35.doi: 10.11959/j.issn.1000-436X.2020223

• 学术论文 • 上一篇    下一篇

基于子图演化与改进蚁群优化算法的社交网络链路预测方法

顾秋阳1,2,3, 琚春华4, 吴功兴4   

  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 宁波诺丁汉大学商学院,浙江 宁波 315175
    4 浙江工商大学管理工程与电子商务学院,浙江 杭州 310018
  • 修回日期:2020-09-05 出版日期:2020-12-25 发布日期:2020-12-01
  • 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。
    琚春华(1962- ),男,博士,浙江衢州人,浙江工商大学教授、博士生导师,主要研究方向为智能信息处理、数据挖掘、电子商务与物流优化等。
    吴功兴(1974- ),男,博士,浙江义乌人,浙江工商大学副教授,主要研究方向为智能信息处理、数据挖掘、电子商务与物流优化等。
  • 基金资助:
    国家自然科学基金资助项目(71571162);浙江省社科规划重点课题基金资助项目(20NDJC10Z);浙江省自然科学基金资助项目(LQ20G010002)

Social network link prediction method based on subgraph evolution and improved ant colony optimization algorithm

Qiuyang GU1,2,3, Chunhua JU4, Gongxing WU4   

  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 Business School, University of Nottingham Ningbo, Ningbo 315175, China
    4 School of Management Science &Engineering, Zhejiang Gongshang University, Hangzhou 310018, China
  • Revised:2020-09-05 Online:2020-12-25 Published:2020-12-01
  • Supported by:
    The National Natural Science Foundation of China(71571162);Zhejiang Social Science Planning Key Projects Fundation(20NDJC10Z);The Natural Science Foundation of Zhejiang Province(LQ20G010002)

摘要:

基于改进蚁群优化算法与子图演化,提出了一种新型非监督社交网络链路预测(SE-ACO)方法。该方法首先在社交网络图中确定特殊子图;然后研究子图演化以预测图中的新链接,并用蚁群优化算法定位特殊子图;最后针对所提方法使用不同网络拓扑环境与数据集进行检验。结果表明,与其他无监督社交网络预测算法相比,所提SE-ACO方法在多数数据集上的评估结果较好,且运行时间较短,这表明图形结构在链路预测算法中起重要作用。

关键词: 链路预测, 蚁群优化算法, 社交网络, 子图演化

Abstract:

Based on improved ant colony algorithm and subgraph evolution fusion, a new unsupervised social network link prediction method (SE-ACO) was proposed.First, the special subgraph was determined in the social network graph.Then the evolution of the subgraph was studied to predict the new links in the graph, and the special subgraph was located by the ant colony method.Finally, using different network topology environments and data sets to test the proposed method.Compared with other unsupervised social network prediction algorithms, the proposed SE-ACO method has the best evaluation results, shorter running time and the best effect on most data sets, which indicates that graph structure plays an important role in link prediction algorithm.

Key words: link prediction, ant colony optimization algorithm, social network, subgraph evolution

中图分类号: 

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