  The Random Geometric Graph (RGG) is a random graph model for network data
with an underlying spatial representation. Geometry endows RGGs with a rich
dependence structure and often leads to desirable properties of real-world
networks such as the small-world phenomenon and clustering. Originally
introduced to model wireless communication networks, RGGs are now very popular
with applications ranging from network user profiling to protein-protein
interactions in biology. RGGs are also of purely theoretical interest since the
underlying geometry gives rise to challenging mathematical questions. Their
resolutions involve results from probability, statistics, combinatorics or
information theory, placing RGGs at the intersection of a large span of
research communities. This paper surveys the recent developments in RGGs from
the lens of high dimensional settings and non-parametric inference. We also
explain how this model differs from classical community based random graph
models and we review recent works that try to take the best of both worlds. As
a by-product, we expose the scope of the mathematical tools used in the proofs.
