A methodology for identifying breakthrough topics using structural entropy. Issue 2 (March 2022) - Xu, Haiyun
Highlights: 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. Abstract: 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.