基于加权非负矩阵分解的链接预测算法

doi:10.3969/j.issn.0372-2112.2016.10.016

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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-meng^1,2, ZUO Wan-li^1,2, WANG Ying^1,2

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.

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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