Modeling propagation dynamics on networks is an amazingly fertile and active area of research. Roughly speaking, network models aim at gaining a better understanding of how actors influence the overall network behavior through their individual actions. Models typically consist in specifying a finite number of algorithmic rules from which overall structural trends can be derived. One is entitled to think that moving beyond the state-of-the-art in network modeling requires the ability to compare models, not only looking at their performance and suitability, but at a fundamental level. This ambitious goal requires having a common language describing models, allowing to objectively compare them and unfold their inherent properties and complexity. The results we present aim at providing a common framework turning network propagation modeling into rule-based modeling (aka graph rewriting). That is, models are described as a set of algorithmic rules acting locally. We show the validity of our approach by providing a description of the well-known model proposed by Goyal et al. 2010 relying on probabilistic rules, where nodes trigger actions depending on their neighbor's influences. Rule-based modeling not only provides a common language to define, describe and build models. It also paves the road to a formal setting from which model simulations can be steered. Because the application of rules is stochastic and non-deterministic, different variations of a model can be defined and easily compared. Our approach is moreover supported through the visual framework PORGY, turning model validation and comparison into a game where one iterates transformation rules on an initial graph, until some condition is met. The results we have obtained using Goyal's model confirm rule-based modeling as a promising avenue. Extending its application to other models will show its use as a common, if not universal, formal language to define and describe network propagation models.