Prediction methods and applications in the science of science: A survey

被引：24

作者：

Hou, Jie ^{[1
]}

Pan, Hanxiao ^{[1
]}

Guo, Teng ^{[1
]}

Lee, Ivan ^{[2
]}

Kong, Xiangjie ^{[1
]}

Xia, Feng ^{[1
]}

机构：

[1] Dalian Univ Technol, Sch Software, Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian, Peoples R China

[2] Univ South Australia, Sch ITMS, Mawson Lakes, SA 5095, Australia

来源：

COMPUTER SCIENCE REVIEW | 2019年 / 34卷

关键词：

Prediction methods; Data analysis; Scholarly data; Science of science; LINK-PREDICTION; CITATION DISTRIBUTIONS; RISING STARS; CO-AUTHOR; IMPACT; UNIVERSALITY; EVOLUTION; INDIVIDUALS; STATISTICS; NETWORKS;

D O I：

10.1016/j.cosrev.2019.100197

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Science of science has become a popular topic that attracts great attentions from the research community. The development of data analytics technologies and the readily available scholarly data enable the exploration of data-driven prediction, which plays a pivotal role in finding the trend of scientific impact. In this paper, we analyse methods and applications in data-driven prediction in the science of science, and discuss their significance. First, we introduce the background and review the current state of the science of science. Second, we review data-driven prediction based on paper citation count, and investigate research issues in this area. Then, we discuss methods to predict scholar impact, and we analyse different approaches to promote the scholarly collaboration in the collaboration network. This paper also discusses open issues and existing challenges, and suggests potential research directions. (C) 2019 Elsevier Inc. All rights reserved.

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