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

• Published in: Journal of computer science and technology 2015-07, Vol.30 (4), p.829-842
• Main Author: 贾岩涛 王元卓 程学旗
• Format: Article
• Language: English
• 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
• ISSN: 1000-9000; 1860-4749
• DOI: 10.1007/s11390-015-1563-9

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.