How can we use the higher-order effects of flow pathways to understand the organization of social and biological systems?
In our connected world, we need efficient maps of flow pathways – for example, how information moves through social and biological systems. To understand flu outbreaks, how animal species move between different domains and other critical organizational phenomena, researchers use tools from network science.
Today researchers rely on simple one-step models of flows on networks. This approach ignores that the flow direction often depends on more than a single step, that is, where the flows come from. We know that such higher-order information about real flow pathways is critical for capturing all-important phenomena in the function of the system.
This exposes a shortcoming of conventional approaches, and raises a major scientific question:
How can we use the higher-order effects of flow pathways to understand the continuously changing organization of social and biological systems?
Conventional methods shoehorn interactions in a complex system into a network, which sets a limit on what we can learn about the system. My research is focused on breaking this detectability limit by using higher-order modeling and mapping of flow pathways. Going beyond networks enables us to take advantage of today's data explosion and create a tool that is to complex research what telescopes are to astronomers and microscopes are to biologists.