符号网络中融合聚集系数与符号影响力的链路预测算法
作者:
作者单位:

1.东北石油大学;2.燕山大学

作者简介:

通讯作者:

中图分类号:

TP301.6

基金项目:

国家自然科学基金(42002138); 黑龙江省自然科学基金(LH2019F042); 东北石油大学青年基金(2018QNQ01); 东北石油大学优秀中青年科研创新团队培育基金(KYCXTDQ202101)


Link prediction algorithm in signed networks based on clustering coefficient and sign influence
Author:
Affiliation:

1.Northeast Petroleum University;2.Yanshan University

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    为快速、准确地实现符号社会网络中的链接预测与符号预测双重目标,提出一种融合共同邻居节点的聚集系数与连边符号影响力的链路预测算法。基于结构平衡理论,有效利用节点的度、聚集系数、路径上的中间传输节点、连边符号及其影响力等信息,分别定义了两节点基于一阶共同邻居和二阶共同邻居的相似性,最终得到两节点的总相似性得分,用其绝对值度量两节点建立链接的可能性,通过其符号获得链接的符号预测结果,从而实现符号网络中的链路预测。在6个有代表性的符号网络数据集上进行了实验,以AUC、调整的Precision’、Accuracy等为评价指标,对比了多个符号网络链接预测算法,并进行了可调步长参数的敏感性分析。实验结果表明,所提算法在符号网络链接预测与符号预测两方面均达到了较好的性能,无论是稀疏网络还是负链接预测,准确性均高于其他算法。

    Abstract:

    In order to achieve the dual goals of link prediction and sign prediction in signed social networks quickly and accurately, a link prediction algorithm is proposed based on the clustering coefficient of common neighbor nodes and the influence of the sign of edges. With the structural balance theory, the similarity of the two nodes based on their first-order common neighbors and the second-order common neighbors is defined respectively by using the degree, clustering coefficient, intermediate transitive nodes, and the influence of the sign of the edge, the total similarity score of the two nodes is finally obtained and its absolute value is used to measure the possibility to establish a link of the two nodes, then its sign is the sign prediction result of the link. Accordingly, the link prediction and sign prediction are realized in signed networks. Experiments have been carried out on six representative signed network datasets, with evaluation indicators such as AUC, adjusted precision' and accuracy. The experiment results are compared with several link prediction algorithms in signed networks the sensitivity of adjustable step size parameters is also analyzed. Experimental results show that the proposed algorithm can achieve good performance in both link prediction and sign prediction, and its accuracy is higher than other algorithms for both sparse networks and the prediction of negative links.

    参考文献
    相似文献
    引证文献
引用本文

引用本文格式: 刘苗苗,扈庆翠,郭景峰,陈晶. 符号网络中融合聚集系数与符号影响力的链路预测算法[J]. 四川大学学报: 自然科学版, 2021, 58: 052003.

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2020-10-11
  • 最后修改日期:2021-01-27
  • 录用日期:2021-03-31
  • 在线发布日期: 2021-10-18
  • 出版日期: