TY - JOUR
T1 - Scholar2vec
T2 - Vector Representation of Scholars for Lifetime Collaborator Prediction
AU - Wang, Wei
AU - Xia, Feng
AU - Wu, Jian
AU - Gong, Zhiguo
AU - Tong, Hanghang
AU - Davison, Brian D.
N1 - Funding Information:
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. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2021 Association for Computing Machinery. 1556-4681/2021/04-ART40 $15.00 https://doi.org/10.1145/3442199
Publisher Copyright:
© 2021 Association for Computing Machinery.
PY - 2021/4
Y1 - 2021/4
N2 - 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.
AB - 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.
KW - Network embedding
KW - academic information retrieval
KW - graph learning
KW - scientific collaboration
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U2 - 10.1145/3442199
DO - 10.1145/3442199
M3 - Article
AN - SCOPUS:85105489914
SN - 1556-4681
VL - 15
JO - ACM Transactions on Knowledge Discovery from Data
JF - ACM Transactions on Knowledge Discovery from Data
IS - 3
M1 - 40
ER -