Title
Shifu2: A Network Representation Learning Based Model for Advisor-Advisee Relationship Mining
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.
Year
DOI
Venue
2021
10.1109/TKDE.2019.2946825
IEEE Transactions on Knowledge and Data Engineering
Keywords
DocType
Volume
Social network analysis,relation extraction,network representation learning,scientific collaboration network,advisor-advisee relationship
Journal
33
Issue
ISSN
Citations 
4
1041-4347
1
PageRank 
References 
Authors
0.35
0
7
Name
Order
Citations
PageRank
Jiaying Liu186083.96
Feng Xia22013153.69
Lei Wang311.36
Bo Xu412.04
Xiangjie Kong542546.56
Hanghang Tong63560202.37
Irwin King76751325.94