A methodology for identifying breakthrough topics using structural entropy

文献类型： 外文期刊

第一作者： Xu, Haiyun

作者： Xu, Haiyun;Luo, Rui;Winnink, Jos;Wang, Chao;Elahi, Ehsan

作者机构：

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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