Mining Advisor-Advisee Relationships in Scholarly Big Data

Abstract

Mining advisor-advisee relationships can benefit many interesting applications such as advisor recommendation and protege performance analysis. Based on the hypothesis that, advisor-advisee relationships among researchers are hidden in scholarly big data, we propose in this work a deep learning based advisor-advisee relationship identification method which considers the personal properties and network characteristics with a stacked autoencoder model. To the best of our knowledge, this is the first time that a deep learning model is utilized to represent coauthor network features for relationships identification. Moreover, experiments demonstrate that the proposed method has better performance compared with other state-of-the-art methods.