Name
Title | ||
|---|---|---|
Mining Advisor-Advisee Relationships in Scholarly Big Data: A Deep Learning Approach. |
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2910896.2925435 | JCDL |
Keywords | Field | DocType |
Deep learning,Relationship mining,Stacked autoencoders | Data science,Data modeling,Protégé,Instance-based learning,Autoencoder,Active learning (machine learning),Computer science,Unsupervised learning,Artificial intelligence,Deep learning,Big data,Machine learning | Conference |
ISSN | ISBN | Citations |
2575-7865 | 978-1-5090-5254-7 | 6 |
PageRank | References | Authors |
0.44 | 1 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Wang | 1 | 7122 | 746.33 |
| Jiaying Liu | 2 | 860 | 83.96 |
| Shuo Yu | 3 | 23 | 3.75 |
| Chenxin Zhang | 4 | 43 | 6.73 |
| Zhenzhen Xu | 5 | 80 | 11.66 |
| Feng Xia | 6 | 2013 | 153.69 |