The study is devoted to the paradigm of rule based computing involving granular information. By information granules we mean a general category of data embracing not only numeric entities (inputs) but any granules (such as intervals or fuzzy sets, in general) being regarded as inputs in the rule-based system. We investigate several categories of models of granularity propagation starting from those based on the use of the mechanisms of possibility and necessity theory, especially possibility and possibility-necessity mechanisms. We also consider the models relying on the use of auxiliary regression models. These models are constructed on the basis of some experimental granular data. A thorough comparative analysis of the introduced models is carried out as well.