Simulating the structure of complex networks is an important challenge for problems ranging from the modeling organizational structure to understanding the behavior of protein aggregates. While recent years have seen many innovations in this area, obtaining provably high quality simulations for networks with complex dependence has remained an elusive goal. In a forthcoming paper in the Journal of Mathematical Sociology, NCASD Lab PI Carter Butts shows how this can be done. Introducing a family of simulation algorithms inspired by a technique called “coupling from the past,” Butts demonstrates the feasibility of obtaining exact draws from even highly complex sampling distributions, avoiding the sometimes problematic approximations inherent in current methods. Because these algorithms can be used for a very broad class of network models, they can be applied in a wide range of settings both within and beyond the social sciences. This work illustrates the potential for computational methods to extend the reach of scientific practice, a major theme of research in the NCASD Lab.