Interest in algorithms for community detection in networked systems has increased over the last decade, mostly motivated by a search for scalable solutions capable of handling large-scale networks. Multilevel approaches provide a potential solution to scalability, as they reduce the cost of a community detection algorithm by applying it to a coarsened version of the original network. The small-scale solution thus obtained is then projected back to the original large-scale model to obtain the desired solution. However, standard multilevel methods are not directly applicable to bipartite network models and the literature lacks studies on multilevel optimization applied to such networks. This article addresses this gap and introduces a novel multilevel method based on onemode projection that allows executing traditional multilevel methods in bipartite network models. The approach has been validated with an algorithm that solves the Barber’s modularity problem. It attained improved runtime performance, whilst solution accuracy is shown to be statistically equivalent to that of the standard method.