Here, we formalize the concept of model reaction with a fixed graph-specificity and benchmark its use in practical activation energy and transition state localization activities. There are several advantages of this graph-based definition of model reactions. First, the graph-based definition does not rely on any specific heuristics and is, therefore, extensible to new chemical spaces. Second, every reaction, regardless of size or complexity, has only one simpler (or equivalent) graphical model. This constrains the cost of characterizing models of specific reactions and it also limits the number of model reactions required to span a reaction space. Third, even the most aggressive simplifications for graphically defined models tend to be much more complex than standard heuristic model compound definitions. Take the well-known Diels-Alder (DA) reaction for example, the simplest DA reaction (i.e. ethene reacts with 1,3-butadiene) serves as a reaction template for all DA reactions in Kinbot and RMG, regardless of substitutions (Fig. ). On the contrary, in total, around 30k CHON model reactions can be enumerated for the DA reaction, (some examples shown in Fig. ). It is observed that the substitutions dramatically change the activation energy and TS geometry, which pose a challenge when using the simplest DA reaction to predict all possible DA reactions. Finally the advantages and limitations of graphically defined model reactions are tested for a wide range of organic reactions, from simple to complex and from closed-shell to open-shell systems. A final application to alkane pyrolysis network prediction is provided to illustrate how the model reactions developed here provide fast yet accurate activation energy predictions in chemical reaction networks.