The ability to control complex networks is of crucial importance across a wide range of applications in natural and engineering sciences. However, issues of both theoretical and numerical nature introduce fundamental limitations to controlling large-scale networks. In this article, we cope with this problem by introducing a coarse-graining algorithm. It leads to an aggregated network which satisfies control equivalence, i.e., such that the optimal control values for the original network can be exactly recovered from those of the aggregated one. The algorithm is based on a partition refinement method originally devised for systems of ordinary differential equations, here extended and applied to linear dynamics on complex networks. Using a number of benchmarks from the literature we show considerable reductions across a variety of networks from biology, ecology, engineering, and social sciences.