Data Science Fellow
Michigan Institute for Data Science
Patrick’s research interest centers on the structure and evolution of large-scale human social networks. His recent work aims to address how social networks adapt in the long term to heightened uncertainty caused by sudden and often unforeseen societal shocks, such as economic busts, corporate M&As and scandals, geopolitical conflicts, and natural disasters. When an area experiences a devastating storm, for example, do people turn to friends and family or acquaintances for information and support? How does the structure of people’s networks influence their communication patterns in response to such societal shocks? How do social networks respond differently depending on the nature of the shock (e.g., political coups vs. hurricanes)? These questions hold increasing significance as societies face mounting uncertainties due to climate change and computation-driven transformations in labor markets and industries. Understanding how social networks change amid heightened uncertainty, then is key to understanding and predicting important social, political, and economic processes that are shaped by social networks, from information diffusion, political polarization, to technological innovation. Patrick investigates these questions around uncertainty and network change, first, by empirically exploring the change and recovery of interpersonal communication networks after societal shocks using social media data (e.g., Twitter) and, second, by devising computational models to theorize the macro-structural implications of the changes in individual-level communication behaviors induced by the shocks.