Directed collaboration patterns in funded teams: a perspective of knowledge flow

• Main Authors: Zou, Bentao, Wang, Yuefen, Kwoh, Chee Keong, Cen, Yonghua
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2023
• Subjects: Engineering::Computer science and engineering; Motif Detection; Collaboration Patterns
• Online Access: https://hdl.handle.net/10356/172460
• Institution: Nanyang Technological University

Collaborations in funded teams are essential for understanding funded research and funding policies, although of high interest, are still not fully understood. This study aims to investigate directed collaboration patterns from the perspective of the knowledge flow, which is measured based on the academic age. To this end, we proposed a project-based team identification approach, which gives particular attention to funded teams. The method is applicable to other funding systems. Based on identified scientific teams, we detected recurring and significant subgraph patterns, known as network motifs, and under-represented patterns, known as anti-motifs. We found commonly occurred motifs and anti-motifs are remarkably characterized by different structures matching certain functions in knowledge exchanges. Collaboration patterns represented by motifs favor hierarchical structures, supporting intensive interactions across academic generations. Anti-motifs are more likely to show chain-like structures, hindering potentially various knowledge activities, and are thus seldom found in real collaboration networks. These findings provide new insights into the understanding of funded collaborations and also the funding system. Meanwhile, our findings are helpful for researchers, the public and policymakers to gain knowledge on research(ers) evolution, particularly in terms of primordial collaboration patterns.