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. Notes on methodology: First, text data were imported into Clarivate’s Derwent Data Analyzer, and the multi-word list in the field of “combined keywords + phrase” was selected as the field-of-topic term. The list in the “combined keywords + phrase” field was extracted from titles…
Identifying a scientific breakthrough early and helping to establish forward-looking predictions.
Depicting the non-linear characteristics of complex knowledge networks through structural changes.
Regarding the knowledge network as a complex system from a holistic perspective.
Observing the incubation mechanism of emergent scientific breakthroughs from a dynamic evolutionary perspective.
Author(s): Haiyun Xu, Rui Luo, Jos Winnink, Chao Wang, Ehsan Elahi
Organization(s): Shandong University of Technology, Jiangsu Academy of Agricultural Sciences, Leiden University
Source: Information Processing & Management