studying here.

In this work, we use the structure of the random geometric graph to evaluate the upper tail large deviation rate function. In addition, we provide a “structure theorem" to describe the graph-theoretical structure of the model conditioned on having too many edges. Specifically, we show that such a conditional model exhibits [*localization*]{}. Heuristically, this phenomenon can be described as a scenario in which a small number of vertices will contribute almost all the extra edges that we require the graph to exhibit, while the edge count of the “bulk” of the graph of the graph will remain largely unchanged, in some weak sense. Furthermore, we will show that the geometry of the localized region has the shape of a ball in the given norm (we will make these two statements more precise at the end of the next section). Outside of the aforementioned work of Lubetzky and Zhao [@Lubetzky] in the Erdős–Rényi model, which is only known for a limited range of $p$’s, this work is the first (as far as the authors are aware) to establish that the large deviation regime of a subgraph count is (weakly) equivalent to planting a combinatorial structure in the usual, unconditional 