Learning to Predict Links by Integrating Structure and Interaction Information in Microblogs

Link prediction in microblogs by using unsupervised methods has been studied extensively in recent years, which aims to find an appropriate similarity measure between users in the network. However, the measures used by existing work lack a simple way to incorporate the structure of the network and t...

Full description

Saved in:
Bibliographic Details
Published inJournal of computer science and technology Vol. 30; no. 4; pp. 829 - 842
Main Author 贾岩涛 王元卓 程学旗
Format Journal Article
LanguageEnglish
Published New York Springer US 01.07.2015
Springer Nature B.V
Subjects
Artificial Intelligence
Blogs
Computer Science
Data Structures and Information Theory
Factorization
Ground truth
Hyperlinks
Information Systems Applications (incl.Internet)
Learning
Links
Mathematical models
Methods
Networks
Regular Paper
Science
Similarity
Similarity measures
Social networks
Software Engineering
State of the art
Studies
Theory of Computation
交互信息
学习
相互作用
相似性度量
结合结构
网络结构
链接
预测
United States > US
matrix factorization
structure-interaction
microblog
retweeting similarity
link prediction
Online AccessGet full text

Cover

More Information
Summary:Link prediction in microblogs by using unsupervised methods has been studied extensively in recent years, which aims to find an appropriate similarity measure between users in the network. However, the measures used by existing work lack a simple way to incorporate the structure of the network and the interactions between users. This leads to the gap between the predictive result and the ground truth value. For example, the F 1-measure created by the best method is around 0.2. In this work, we firstly discover the gap and prove its existence. To narrow this gap, we define the retweeting similarity to measure the interactions between users in Twitter, and propose a structural-interaction based matrix factorization model for following-link prediction. Experiments based on the real-world Twitter data show that our model outperforms state-of-the-art methods.
Bibliography:Link prediction in microblogs by using unsupervised methods has been studied extensively in recent years, which aims to find an appropriate similarity measure between users in the network. However, the measures used by existing work lack a simple way to incorporate the structure of the network and the interactions between users. This leads to the gap between the predictive result and the ground truth value. For example, the F 1-measure created by the best method is around 0.2. In this work, we firstly discover the gap and prove its existence. To narrow this gap, we define the retweeting similarity to measure the interactions between users in Twitter, and propose a structural-interaction based matrix factorization model for following-link prediction. Experiments based on the real-world Twitter data show that our model outperforms state-of-the-art methods.
11-2296/TP
link prediction, microblog, structure-interaction, retweeting similarity, matrix factorization
Yan-Tao Jia, Yuan-Zhuo Wang,Xue-Qi Cheng（Key Laboratory of Network Science and Technology, Institute of Computing Technology Chinese Academy of Sciences, Beijing 100190, China）
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1000-9000
1860-4749
DOI:10.1007/s11390-015-1563-9