Coauthor prediction for junior researchers

Han, S and He, D and Brusilovsky, P and Yue, Z (2013) Coauthor prediction for junior researchers. In: UNSPECIFIED.

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Abstract

Research collaboration can bring in different perspectives and generate more productive results. However, finding an appropriate collaborator can be difficult due to the lacking of sufficient information. Link prediction is a related technique for collaborator discovery; but its focus has been mostly on the core authors who have relatively more publications. We argue that junior researchers actually need more help in finding collaborators. Thus, in this paper, we focus on coauthor prediction for junior researchers. Most of the previous works on coauthor prediction considered global network feature and local network feature separately, or tried to combine local network feature and content feature. But we found a significant improvement by simply combing local network feature and global network feature. We further developed a regularization based approach to incorporate multiple features simultaneously. Experimental results demonstrated that this approach outperformed the simple linear combination of multiple features. We further showed that content features, which were proved to be useful in link prediction, can be easily integrated into our regularization approach. © 2013 Springer-Verlag.

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Details

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Status:	Published
Creators/Authors:	Creators Email Pitt Username ORCID Han, S shh69@pitt.edu SHH69 He, D dah44@pitt.edu DAH44 0000-0002-4645-8696 Brusilovsky, P peterb@pitt.edu PETERB 0000-0002-1902-1464 Yue, Z
Date:	14 March 2013
Date Type:	Publication
Access Restriction:	No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title:	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume:	7812 L
Page Range:	274 - 283
Event Type:	Conference
DOI or Unique Handle:	10.1007/978-3-642-37210-0_30
Institution:	University of Pittsburgh
Schools and Programs:	School of Information Sciences > Information Science
Refereed:	Yes
ISBN:	9783642372094
ISSN:	0302-9743
Date Deposited:	14 Jan 2014 15:26
Last Modified:	01 Jul 2022 11:56
URI:	http://d-scholarship.pitt.edu/id/eprint/20337

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