A methodology for identifying breakthrough topics using structural entropy. Issue 2 (March 2022)

• Record Type: Journal Article
• Authors: Xu, Haiyun; Luo, Rui; Winnink, Jos; Wang, Chao; Elahi, Ehsan
• Is Part Of: Information processing & management. Volume 59:Issue 2(2022)
• Publication Date: 2022-03
• Subjects: structural entropy -- scientific breakthrough -- link prediction -- knowledge networks; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2021.102862
• Languages: English
• ISSNs: 0306-4573

Abstract

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.