Identifying the Academic Rising Stars via Pairwise Citation Increment Ranking

Handle URI:
http://hdl.handle.net/10754/626788
Title:
Identifying the Academic Rising Stars via Pairwise Citation Increment Ranking
Authors:
Zhang, Chuxu; Liu, Chuang; Yu, Lu; Zhang, Zi-Ke; Zhou, Tao
Abstract:
Predicting the fast-rising young researchers (the Academic Rising Stars) in the future provides useful guidance to the research community, e.g., offering competitive candidates to university for young faculty hiring as they are expected to have success academic careers. In this work, given a set of young researchers who have published the first first-author paper recently, we solve the problem of how to effectively predict the top k% researchers who achieve the highest citation increment in Δt years. We explore a series of factors that can drive an author to be fast-rising and design a novel pairwise citation increment ranking (PCIR) method that leverages those factors to predict the academic rising stars. Experimental results on the large ArnetMiner dataset with over 1.7 million authors demonstrate the effectiveness of PCIR. Specifically, it outperforms all given benchmark methods, with over 8% average improvement. Further analysis demonstrates that temporal features are the best indicators for rising stars prediction, while venue features are less relevant.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Citation:
Zhang C, Liu C, Yu L, Zhang Z-K, Zhou T (2017) Identifying the Academic Rising Stars via Pairwise Citation Increment Ranking. Lecture Notes in Computer Science: 475–483. Available: http://dx.doi.org/10.1007/978-3-319-63579-8_36.
Publisher:
Springer International Publishing
Journal:
Lecture Notes in Computer Science
Conference/Event name:
1st Asia-Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2017
Issue Date:
2-Aug-2017
DOI:
10.1007/978-3-319-63579-8_36
Type:
Conference Paper
ISSN:
0302-9743; 1611-3349
Sponsors:
This work was partially supported by Natural Science Foundation of China (Grant Nos. 61673151, 61503110 and 61433014), Zhejiang Provincial Natural Science Foundation of China (Grant Nos. LY14A050001 and LQ16F030006).
Additional Links:
https://link.springer.com/chapter/10.1007%2F978-3-319-63579-8_36
Appears in Collections:
Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorZhang, Chuxuen
dc.contributor.authorLiu, Chuangen
dc.contributor.authorYu, Luen
dc.contributor.authorZhang, Zi-Keen
dc.contributor.authorZhou, Taoen
dc.date.accessioned2018-01-15T06:35:09Z-
dc.date.available2018-01-15T06:35:09Z-
dc.date.issued2017-08-02en
dc.identifier.citationZhang C, Liu C, Yu L, Zhang Z-K, Zhou T (2017) Identifying the Academic Rising Stars via Pairwise Citation Increment Ranking. Lecture Notes in Computer Science: 475–483. Available: http://dx.doi.org/10.1007/978-3-319-63579-8_36.en
dc.identifier.issn0302-9743en
dc.identifier.issn1611-3349en
dc.identifier.doi10.1007/978-3-319-63579-8_36en
dc.identifier.urihttp://hdl.handle.net/10754/626788-
dc.description.abstractPredicting the fast-rising young researchers (the Academic Rising Stars) in the future provides useful guidance to the research community, e.g., offering competitive candidates to university for young faculty hiring as they are expected to have success academic careers. In this work, given a set of young researchers who have published the first first-author paper recently, we solve the problem of how to effectively predict the top k% researchers who achieve the highest citation increment in Δt years. We explore a series of factors that can drive an author to be fast-rising and design a novel pairwise citation increment ranking (PCIR) method that leverages those factors to predict the academic rising stars. Experimental results on the large ArnetMiner dataset with over 1.7 million authors demonstrate the effectiveness of PCIR. Specifically, it outperforms all given benchmark methods, with over 8% average improvement. Further analysis demonstrates that temporal features are the best indicators for rising stars prediction, while venue features are less relevant.en
dc.description.sponsorshipThis work was partially supported by Natural Science Foundation of China (Grant Nos. 61673151, 61503110 and 61433014), Zhejiang Provincial Natural Science Foundation of China (Grant Nos. LY14A050001 and LQ16F030006).en
dc.publisherSpringer International Publishingen
dc.relation.urlhttps://link.springer.com/chapter/10.1007%2F978-3-319-63579-8_36en
dc.subjectBayesian personalized rankingen
dc.subjectData engineeringen
dc.subjectScientific impact predictionen
dc.titleIdentifying the Academic Rising Stars via Pairwise Citation Increment Rankingen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journalLecture Notes in Computer Scienceen
dc.conference.date2017-07-07 to 2017-07-09en
dc.conference.name1st Asia-Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2017en
dc.conference.locationBeijing, CHNen
dc.contributor.institutionDepartment of Computer Science and Engineering, University of Notre Dame, Notre Dame, United Statesen
dc.contributor.institutionAlibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou, Chinaen
dc.contributor.institutionBig Data Research Center, University of Electronic Science and Technology of China, Chengdu, Chinaen
kaust.authorYu, Luen
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