Identifying advisor-advisee relationships from co-author networks via a novel deep model
Authors: Zhongying Zhao, Wenqiang Liu, Yuhua Qian, Liqiang Nie, Yilong Yin, Yong Zhang
Abstract:
Authors: Zhongying Zhao, Wenqiang Liu, Yuhua Qian, Liqiang Nie, Yilong Yin, Yong Zhang
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 advisoradvisee 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.
Identifying advisor-advisee relationships from co-author networks via a novel deep model
Keywords：
identifying advisor-advisee relationships from co-author networks via a novel deep model.pdf
Wed Sep 05 00:00:00 CST 2018