Rich gets simpler.

Network science plays a central role in the study of complex systems, offering a range of computational tools and, importantly, a common language to represent systems as diverse as the World Wide Web, the human brain, and social networks (1). Within the framework of network science, a system is modeled as a set of nodes, representing the individual units of the system, and a set of links, representing the dyadic relationships between these units. Many networks have been shown to exhibit a complex organization and yet can often be comprehended by simple and universal mechanisms. However, to properly capture the complexity of real-world interacting systems, standard network models are sometimes not sufficient. For this reason, different attempts have been made to enrich the network language in recent years. Important examples include multiplex or multilayer networks (2), where different types of interactions are accounted for, higher-order networks (3–5), focusing on pathways instead of dyadic interactions, and temporal networks (6, 7), where nodes and links become dynamical entities. In PNAS, Sekara et al. (8) make two important contributions to the latter approach, first by studying a longitudinal, high-resolution dataset on human interactions over an extended time window and second by showing that significant structural patterns naturally emerge from the system when considered at an appropriate time scale (Fig. 1)