Journal
2016 IEEE/ACM JOINT CONFERENCE ON DIGITAL LIBRARIES (JCDL)
Volume -, Issue -, Pages 209-210Publisher
IEEE
DOI: 10.1145/2910896.2925435
Keywords
Deep learning; Relationship mining; Stacked autoencoders
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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.
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