Higher-Order Network Approaches to Science of Science, Temporal Group Dynamics and the Diffusion of Innovation
Abstract
Scientific progress is at the heart of our society. Collaboration, innovation, and cooperÂative behavior naturally arise from social interactions. Over the past decades, complex social systems have been overwhelmingly described through the language of networks, where interacting pairs of nodes are connected by links. Yet, empirical evidence suggests that in many such systems, individuals routinely interact in larger groups, and that the cumulative effect on knowledge growth and information spreading of such group interactions cannot be achieved as a combination of dyadic ties. Going beyond intrinsically limited network descriptions, the grand challenge of this project is to advance our understanding of innovation and cooperation patterns arising in evolvÂing scientific and technological ecosystems by leveraging the new theory of higher-order interaction networks. We will first identify the higher-order structural features of social and collaboration networks associated with different disciplines. We will then introduce new temporal higher-order models able to reproduce the empirical findings on team assembly in the wider scientific ecosystem. Finally, we will capture the innovation potential arising from group interactions and provide a data-driven, quantitative description of the higher-order mechanisms associated with the spreading of new information and ideas neglected by traditional methods.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 20, 2023
- Source ID
- FA86552217025
Entities
People
- Federico Battiston
Organizations
- Air Force Office of Scientific Research
- United States Air Force