Modeling propagation dynamics on networks is an amazingly fer-tile and active area of research. Roughly speaking, network models aim at gaining a better understanding of how actors influence the overall network behaviour through their individual actions. How-ever, considering the extended literature surrounding the subject, one is entitled to think that moving beyond the state-of-the-art in network modeling requires the ability to compare models, or con-sider slight variations of a model. This requires having a common language describing all considered models, allowing to objectively compare them and unfold their inherent properties and complex-ity. This also assumes users can easily run models, steer them and interactively evaluate their performance and behaviour. The approach we describe aims at providing a framework turning network propagation modeling into rule-based modeling (aka graph rewriting). That is, models are described as a set of algorithmic transformation rules acting locally. Our approach has partially been validated by providing such a description of a well-known model relying on probabilistic rules, where nodes trigger actions depend-ing on their neighbor's influences. The results so obtained confirm rule-based modeling as a promising avenue. The use of a visual an-alytics framework to conduct such tasks is vital and motivated us to further develop and adapt a general purpose visual analytics system for graph rewriting to the particular case of network propagation.