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...
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Published in | Journal of computer science and technology Vol. 30; no. 4; pp. 829 - 842 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
New York
Springer US
01.07.2015
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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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. |
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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 |