Shifu2: A Network Representation Learning Based Model for Advisor-Advisee Relationship Mining

Jiaying Liu, Feng Xia, Lei Wang, Bo Xu, Xiangjie Kong, Hanghang Tong, Irwin King

Research output: Contribution to journalArticlepeer-review

Abstract

The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines. This work aims to discover advisor-advisee relationships hidden behind scientific collaboration networks. For this purpose, we propose a novel model based on Network Representation Learning (NRL), namely Shifu2, which takes the collaboration network as input and the identified advisor-advisee relationship as output. In contrast to existing NRL models, Shifu2 considers not only the network structure but also the semantic information of nodes and edges. Shifu2 encodes nodes and edges into low-dimensional vectors respectively, both of which are then utilized to identify advisor-advisee relationships. Experimental results illustrate improved stability and effectiveness of the proposed model over state-of-the-art methods. In addition, we generate a large-scale academic genealogy dataset by taking advantage of Shifu2.

Original languageEnglish (US)
Article number8865612
Pages (from-to)1763-1777
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Volume33
Issue number4
DOIs
StatePublished - Apr 1 2021

Keywords

  • advisor-advisee relationship
  • network representation learning
  • relation extraction
  • scientific collaboration network
  • Social network analysis

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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