A methodology for identifying breakthrough topics using structural entropy

被引：33
作者：
Xu, Haiyun [1 ]
Luo, Rui [2 ]
Winnink, Jos [3 ]
Wang, Chao [4 ]
Elahi, Ehsan [5 ]
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
来源：
INFORMATION PROCESSING & MANAGEMENT | 2022年 / 59卷 / 02期
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
structural entropy; scientific breakthrough; link prediction; knowledge networks; COMPLEX NETWORKS; SCIENCE; IDENTIFICATION; RECOGNITION;
D O I：
10.1016/j.ipm.2021.102862
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
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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收藏
页数：20
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