Understanding Mobility Patterns through Patents, Publications, and Temporal Network Causal Inference

Abstract

In this talk, we will explore how patents and scientific publications can be used to infer mobility patterns through network and causal inference methods. By analyzing this information through a network lens, we can uncover patterns of knowledge diffusion and the flow of people and ideas across different fields and regions. Additionally, our novel path analysis tools can identify temporal correlations between mobility patterns and other relevant factors, such as innovation outcomes. Our approach is useful not only for understanding mobility patterns in the context of technology transfer and regional development but also for other fields dealing with temporal network data. This research sheds light on the mechanisms that shape knowledge flows and can inform evidence-based policymaking.