Large-scale graphs are at the core of a plethora of modern applications such as social networks, transportation networks, or the Semantic Web. Such graphs are naturally evolving over time, which makes particularly challenging graph processing tasks e.g., graph mining. To be able to realize rigorous empirical evaluations of research ideas, the graph processing community needs finely-tuned generators of synthetic time-evolving graphs, which are particularly useful whenever real-world graphs are unavailable for public use. The goal of this paper is to report on an ongoing project that aims at generating synthetic time-evolving graphs satisfying finely-tuned temporal constraints specified by the user.