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

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

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

1. 吉林大学计算机科学与技术学院, 吉林长春 130012;
2. 符号计算与知识工程教育部重点实验室(吉林大学), 吉林长春 130012

收稿日期:2015-03-30 修回日期:2015-08-03 出版日期:2016-10-25
- 通讯作者:
- 王英
- 作者简介:
- 王萌萌,女,1987年出生,吉林长春人,2013年至今于吉林大学计算机学院攻读博士学位,从事社会网络分析、自然语言处理等有关研究.E-mail:wmmwwlh@126.com;左万利,男,1957年出生,吉林长春人,博士,教授、博士生导师,从事社会网络分析、网络搜索引擎、自然语言处理等有关研究.E-mail:wanli@jlu.edu.cn
- 基金资助:
- 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）

Link Prediction Model Based on Weighted Nonnegative Matrix Factorization

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

1. College of Compuer Science and Technology, Jilin University, Changchun, Jilin 130012, China;
2. Key Laboratory of Symbolic Computation and Knowledge Engineering(Jilin University), Ministry of Education, Changchun, Jilin 130012, China

Received:2015-03-30 Revised:2015-08-03 Online:2016-10-25 Published:2016-10-25
- Supported by:
- National Natural Science Foundation of China (No.61300148); Science and Technology Development Project of Jilin Province (No.20130206051GX); Science and Technology Program of Jilin Province (No.20130522112JH); Fundamental Fund for Science Frontier and Cross Project of Jilin University (No.201103129)

摘要/Abstract

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.

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

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.

Key words: directed link prediction, nonnegative matrix factorization, features' weights, temporal information, dynamic social networks

中图分类号:

TP393.03

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.

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.

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基于加权非负矩阵分解的链接预测算法

Link Prediction Model Based on Weighted Nonnegative Matrix Factorization

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