- Title
- Scholar2vec : vector representation of scholars for lifetime collaborator prediction
- Creator
- Wang, Wei; Xia, Feng; Wu, Jian; Gong, Zhiguo; Tong, Hanghang; Davison, Brian
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/176887
- Identifier
- vital:15215
- Identifier
-
https://doi.org/10.1145/3442199
- Identifier
- ISBN:1556-4681 (ISSN)
- Abstract
- While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar's academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars' research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborators with machine learning methods. Extensive experiments on two real-world datasets show that Scholar2vec outperforms state-of-the-art methods in lifetime collaborator prediction. Our work presents a new way to measure the similarity between two scholars by vector representation, which tackles the knowledge between network embedding and academic relationship mining. © 2021 Association for Computing Machinery.
- Publisher
- Association for Computing Machinery
- Relation
- ACM Transactions on Knowledge Discovery from Data Vol. 15, no. 3 (2021), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2021 Association for Computing Machinery.
- Rights
- Open Access
- Subject
- 0801 Artificial Intelligence and Image Processing; 0806 Information Systems; Academic information retrieval; Graph learning; Network embedding; Scientific collaboration
- Full Text
- Reviewed
- Funder
- This work was partially supported by the National Natural Science Foundation of China under Grant No. 61872054. Authors’ addresses: W. Wang, School of Software, Dalian University of Technology, Dalian 116620, China, and Faculty of Science and Technology, University of Macau, Macau 999078, China; email: ehome.wang@outlook.com; F. Xia (corresponding author), School of Engineering, IT and Physical Sciences, Federation University Australia, Ballarat 3353, Australia, and School of Software, Dalian University of Technology, Dalian 116620, China; email: f.xia@acm.org; J. Wu, Department of Computer Science, Old Dominion University, Norfolk, VA 23529; email: jwu@cs.odu.edu; Z. Gong, State Key Laboratory of Internet of Things for Smart City, Faculty of Science and Technology, University of Macau, Macau 999078, China; email: fstzgg@um.edu.mo; H. Tong, Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL 61801; email: htong@illinois.edu; B. D. Davison, Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015; email: davison@cse.lehigh.edu. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted.
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