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

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• 作者：Ali Daud ; Muhammad Ahmad ; M. S. I. Malik ; Dunren Che
• 关键词：Group leader ; Classification ; Prediction ; Rising star ; MEMM ; CART
• 刊名：Scientometrics
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：102
• 期：2
• 页码：1687-1711
• 全文大小：1,506 KB
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• 刊物主题：Information Storage and Retrieval; Library Science; Interdisciplinary Studies;
• 出版者：Springer Netherlands
• ISSN：1588-2861

文摘

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 (1996-000 for wide disciplines) are used for algorithms-experimental analysis.