Studying propagation dynamics in networks through rule-based modeling

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 behaviour through their individual actions. However, 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 consider 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 complexity. 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 depending on their neighbor´s influences. The results so obtained confirm rule-based modeling as a promising avenue. The use of a visual analytics 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.