12 Citations (Scopus)

Abstract

Scholars in academia are involved in various social relationships such as advisor-advisee relationships. The analysis of such relationship can provide invaluable information for understanding the interactions among scholars as well as providing many researcher-specific applications such as advisor recommendation and academic rising star identification. However, in most cases, high quality advisor-advisee relationship dataset is unavailable. To address this problem, we propose Shifu, a deep-learning-based advisor-advisee relationship identification method which takes into account both the local properties and network characteristics. In particular, we explore how to crawl advisor-advisee pairs from PhDtree project and extract their publication information by matching them with DBLP dataset as the experimental dataset. To the best of our knowledge, no prior effort has been made to address the scientific collaboration network features for relationship identification by exploiting deep learning. Our experiments demonstrate that the proposed method outperforms other state-of-the-art machine learning methods in precision (94%). Furthermore, we apply Shifu to the entire DBLP dataset and obtain a large-scale advisor-advisee relationship dataset.

Original languageEnglish (US)
Title of host publication26th International World Wide Web Conference 2017, WWW 2017 Companion
PublisherInternational World Wide Web Conferences Steering Committee
Pages303-310
Number of pages8
ISBN (Electronic)9781450349147
DOIs
StatePublished - Jan 1 2019
Event26th International World Wide Web Conference, WWW 2017 Companion - Perth, Australia
Duration: Apr 3 2017Apr 7 2017

Other

Other26th International World Wide Web Conference, WWW 2017 Companion
CountryAustralia
CityPerth
Period4/3/174/7/17

Fingerprint

Stars
Learning systems
Experiments
Big data
Deep learning

Keywords

  • Coauthor network
  • Deep learning
  • Relation mining
  • Scholarly big data

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

Cite this

Wang, W., Liu, J., Xia, F., King, I., & Tong, H. (2019). Shifu: Deep learning based advisor-advisee relationship mining in scholarly big data. In 26th International World Wide Web Conference 2017, WWW 2017 Companion (pp. 303-310). International World Wide Web Conferences Steering Committee. https://doi.org/10.1145/3041021.3054159

Shifu : Deep learning based advisor-advisee relationship mining in scholarly big data. / Wang, Wei; Liu, Jiaying; Xia, Feng; King, Irwin; Tong, Hanghang.

26th International World Wide Web Conference 2017, WWW 2017 Companion. International World Wide Web Conferences Steering Committee, 2019. p. 303-310.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, W, Liu, J, Xia, F, King, I & Tong, H 2019, Shifu: Deep learning based advisor-advisee relationship mining in scholarly big data. in 26th International World Wide Web Conference 2017, WWW 2017 Companion. International World Wide Web Conferences Steering Committee, pp. 303-310, 26th International World Wide Web Conference, WWW 2017 Companion, Perth, Australia, 4/3/17. https://doi.org/10.1145/3041021.3054159
Wang W, Liu J, Xia F, King I, Tong H. Shifu: Deep learning based advisor-advisee relationship mining in scholarly big data. In 26th International World Wide Web Conference 2017, WWW 2017 Companion. International World Wide Web Conferences Steering Committee. 2019. p. 303-310 https://doi.org/10.1145/3041021.3054159
Wang, Wei ; Liu, Jiaying ; Xia, Feng ; King, Irwin ; Tong, Hanghang. / Shifu : Deep learning based advisor-advisee relationship mining in scholarly big data. 26th International World Wide Web Conference 2017, WWW 2017 Companion. International World Wide Web Conferences Steering Committee, 2019. pp. 303-310
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