Existing statistical models for network data that are easy to estimate and fit are based on the assumption of dyad independence or conditional dyad independence if the individuals are categorized into subgroups. We discuss how such models might be overparameterized and argue that there is a need for subgrouping methods to find appropriate models. We propose clustering of dyad distributions as such a method and illustrate it by analyzing how cooperative learning methods affect friendship data for school children.