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A methodology for identifying breakthrough topics using structural entropy

文献类型： 外文期刊

作者： Xu, Haiyun 1 ; Luo, Rui 2 ; Winnink, Jos 3 ; Wang, Chao 4 ; Elahi, Ehsan 5 ;

作者机构： 1.Shandong Univ Technol, Business Sch, Zibo 255000, Peoples R China

2.Jiangsu Acad Agr Sci, Informat Ctr, Nanjing 210014, Peoples R China

3.Leiden Univ, Ctr Sci & Technol Studies CWTS, NL-2300 AX Leiden, Netherlands

4.Shandong Acad Sci, Informat Res Inst, Jinan 250014, Peoples R China

5.Shandong Univ Technol, Sch Econ, Zibo 255049, Shandong, Peoples R China

关键词： structural entropy; scientific breakthrough; link prediction; knowledge networks

期刊名称：INFORMATION PROCESSING & MANAGEMENT （ 影响因子：7.466； 五年影响因子：7.036 ）

ISSN： 0306-4573

年卷期： 2022 年 59 卷 2 期

页码：

收录情况： SCI

摘要： This research uses link prediction and structural-entropy methods to predict scientific breakthrough topics. Temporal changes in the structural entropy of a knowledge network can be used to identify potential breakthrough topics. This has been done by tracking and monitoring a network's critical transition points, also known as tipping points. The moment at which a significant change in the structural entropy of a knowledge network occurs may denote the points in time when breakthrough topics emerge. The method was validated by domain experts and was demonstrated to be a feasible tool for identifying scientific breakthroughs early. This method can play a role in identifying scientific breakthroughs and could aid in realizing forward-looking predictions to provide support for policy formulation and direct scientific research.

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