Graph-theoretical analysis has been widely applied to study the modular organization of brain functional connectivity networks. However, existing methods suffer from a fundamental resolution limit. Here, we propose and validate a novel, resolution-limit-free approach dubbed Asymptotical Surprise. Application of this method to human resting state networks reveals the presence of heterogeneously distributed modules, corresponding to neuroanatomically and functionally plausible networks. The finer partition afforded by Asymptotical Surprise enables a more accurate identification of connector hubs, the brain regions that are thought to be responsible for the integration of functionally segregated modules into a cohesive structure.