基于加权非负矩阵分解的链接预测算法

王萌萌, 左万利, 王英

电子学报 ›› 2016, Vol. 44 ›› Issue (10) : 2391-2397.

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电子学报 ›› 2016, Vol. 44 ›› Issue (10) : 2391-2397. DOI: 10.3969/j.issn.0372-2112.2016.10.016
学术论文

基于加权非负矩阵分解的链接预测算法

  • 王萌萌1,2, 左万利1,2, 王英1,2
作者信息 +

Link Prediction Model Based on Weighted Nonnegative Matrix Factorization

  • WANG Meng-meng1,2, ZUO Wan-li1,2, WANG Ying1,2
Author information +
文章历史 +

摘要

本文针对在线微博,首先,基于带权动态链接预测特征集合,以用户社会关系因子约束目标函数,从用户概要和用户发布内容两个维度利用非负矩阵分解方法预测社会网络中链接的存在性和方向性.然后,在真实的数据集上验证了提出框架的有效性,并通过实验进一步证明了特征权重和时间信息在链接预测问题中的重要性.

Abstract

Targeted at on-line microbloggings,on the basis of weighted and dynamic link prediction features,we utilize nonnegative matrix factorization to predict existence and directivity of link from user-based and post-based dimension by employing relationship-based factor to constrain objective function.Experiments on real-world dataset demonstrate the effectiveness of the proposed framework.Further experiments are conducted to understand the importance of features' weights and temporal information in link prediction.

关键词

有向链接预测 / 非负矩阵分解 / 特征权重 / 时间信息 / 动态社会网络

Key words

directed link prediction / nonnegative matrix factorization / features' weights / temporal information / dynamic social networks

引用本文

导出引用
王萌萌, 左万利, 王英. 基于加权非负矩阵分解的链接预测算法[J]. 电子学报, 2016, 44(10): 2391-2397. https://doi.org/10.3969/j.issn.0372-2112.2016.10.016
WANG Meng-meng, ZUO Wan-li, WANG Ying. Link Prediction Model Based on Weighted Nonnegative Matrix Factorization[J]. Acta Electronica Sinica, 2016, 44(10): 2391-2397. https://doi.org/10.3969/j.issn.0372-2112.2016.10.016
中图分类号: TP393.03   

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基金

国家自然科学基金 (No.61300148); 吉林省科技发展计划 (No.20130206051GX); 吉林省科技计划 (No.20130522112JH); 吉林大学基本科研业务费科学前沿与交叉项目 (No.201103129)

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