Using machine learning techniques for rising star prediction in co-author network

Ali Daud (), Muhammad Ahmad (), M. S. I. Malik () and Dunren Che ()
Additional contact information
Ali Daud: International Islamic University
Muhammad Ahmad: Allama Iqbal Open University
M. S. I. Malik: International Islamic University
Dunren Che: Southern Illinois University

Scientometrics, 2015, vol. 102, issue 2, No 32, 1687-1711

Abstract: Abstract Online bibliographic databases are powerful resources for research in data mining and social network analysis especially co-author networks. Predicting future rising stars is to find brilliant scholars/researchers in co-author networks. In this paper, we propose a solution for rising star prediction by applying machine learning techniques. For classification task, discriminative and generative modeling techniques are considered and two algorithms are chosen for each category. The author, co-authorship and venue based information are incorporated, resulting in eleven features with their mathematical formulations. Extensive experiments are performed to analyze the impact of individual feature, category wise and their combination w.r.t classification accuracy. Then, two ranking lists for top 30 scholars are presented from predicted rising stars. In addition, this concept is demonstrated for prediction of rising stars in database domain. Data from DBLP and Arnetminer databases (1996–2000 for wide disciplines) are used for algorithms’ experimental analysis.

Keywords: Group leader; Classification; Prediction; Rising star; MEMM; CART (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-014-1455-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:102:y:2015:i:2:d:10.1007_s11192-014-1455-8

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-014-1455-8

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().