Wet granulation is an essential process in various industries. In this process, the addition of binder (liquid) leads to the formation of enlarged particle clusters or granules. Due to the complexity of granulation at the micro-level, optimization merely by experiments can be tedious and challenging. Micro-scale numerical simulations, like those based on the Discrete Element Model (DEM), can support the process design. However, the high computational cost of DEM poses challenges for industrial scale simulations. As a potential solution, coarse-graining (CG) approaches significantly improve the computational efficiency of simulations by upscaling groups of original particles to larger CG particles, and thus achieving a new, much faster particle simulation method. In Ref. [1], we showed that in the case of wet dense particle systems, a Weber-based scaling - which leads to quadratic scaling of the inter-particle forces - assures that the flow behaviour is conserved in CG-DEM simulations. Here, we focus on the effect of coarse-graining on the granulation characteristics. For this aim, we combine DEM with graph network analysis (GNA) to identify the granules and obtain the granules volume distribution, number of granules, growth rate, and granulation yield in the original and upscaled particle systems. Using the much faster CG simulations and the derived scaling rules for the granule characteristics, the effect of material and process parameters can be extensively studied. REFERENCES [1] Larijani, R.S., Magnanimo, V. and Luding, S., 2025. A Coarse-Grained Discrete Element Model (CG-DEM) based on parameter scaling for dense wet granular system. Powder Technology, 453, 120581.