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Finding rising stars in bibliometric networks
被引:10
|作者:
Daud, Ali
[1
]
Song, Min
[2
]
Hayat, Malik Khizar
[3
]
Amjad, Tehmina
[3
]
Abbasi, Rabeeh Ayaz
[4
]
Dawood, Hassan
[5
]
Ghani, Anwar
[3
]
机构:
[1] Univ Jeddah, Dept Comp Sci & Artificial Intelligence, Jeddah, Saudi Arabia
[2] Yonsei Univ, Dept Lib & Informat Sci, Seoul, South Korea
[3] IIU, Dept Comp Sci & Software Engn, Islamabad, Pakistan
[4] QAU, Dept Comp Sci, Islamabad, Pakistan
[5] Univ Engn & Technol, Dept Software Engn, Texila, Pakistan
基金:
新加坡国家研究基金会;
关键词:
Finding rising stars (FRS);
Ranking;
Prediction;
Clustering;
Analysis;
Bibliometric networks;
PREDICTION;
D O I:
10.1007/s11192-020-03466-w
中图分类号:
TP39 [计算机的应用];
学科分类号:
081203 ;
0835 ;
摘要:
Finding rising stars (FRS) is a hot research topic investigated recently for diverse application domains. These days, people are more interested in finding people who will become experts shortly to fill junior positions than finding existing experts who can immediately fill senior positions. FRS can increase productivity wherever they join due to their vibrant and energetic behavior. In this paper, we assess the methods to find FRS. The existing methods are classified into ranking-, prediction-, clustering-, and analysis-based methods, and the pros and cons of these methods are discussed. Details of standard datasets and performance-evaluation measures are also provided for this growing area of research. We conclude by discussing open challenges and future directions in this prosperous area of research.
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页码:633 / 661
页数:29
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