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.

引用

页数：20

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