Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6856166 | Information Sciences | 2018 | 12 Pages |
Abstract
Advisor-advisee is one of the most important relationships in research publication networks. Identifying it can benefit many interesting applications, such as double-blind peer review, academic circle mining, and scientific community analysis. However, the advisor-advisee relationships are often hidden in research publication network and vary over time, thus are difficult to detect. In this paper, we present a time-aware Advisor-advisee Relationship Mining Model (tARMM) to better identify such relationships. It is a deep model equipped with improved Refresh Gate Recurrent Units (RGRU). Extensive experiments over real-world DBLP data have well verified the effectiveness of our proposed model.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Zhongying Zhao, Wenqiang Liu, Yuhua Qian, Liqiang Nie, Yilong Yin, Yong Zhang,