Year: 2025
Type: book-chapter
Abstract: The importance of interdisciplinary talent teams is becoming increasingly prominent.Talents of these teams often have multiple identities and belong to several teams simultaneously.In this paper, we employ the methods and theories of complex networks to discover cross-disciplinary talent teams.Firstly, we proposed a unified nonnegative matrix tri-factorization framework, in which the talent association information and research direction information are jointly utilized for collaborative learning and effective integration.Moreover, the talent team relationship matrix is introduced to capture and quantify the tightness of the association between the talent teams, and regularization constraints are applied to ensure the consistency of relationships among talent teams.Finally, a strategy for dividing multidisciplinary and intersecting talent teams is designed, enabling the precise discovery of multidisciplinary talent teams.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410599030