New tool untangles complex dynamics on hypergraphs
Networks are a powerful model for describing connected systems in biological, physical, social, and other environments. As useful as they are, though, conventional networks are static and are limited to describing links between pairs of objects. In a paper published in Communications Physics, SFI Schmidt Science Fellow Yuanzhao Zhang and collaborators describe a new framework for simplifying the analysis of synchronization patterns in a wide variety of systems that include hypergraphs, temporal networks, and multilayer networks.