  Random geometric graphs are random graph models defined on metric spaces.
Such a model is defined by first sampling points from a metric space and then
connecting each pair of sampled points with probability that depends on their
distance, independently among pairs. In this work, we show how to efficiently
reconstruct the geometry of the underlying space from the sampled graph under
the manifold assumption, i.e., assuming that the underlying space is a low
dimensional manifold and that the connection probability is a strictly
decreasing function of the Euclidean distance between the points in a given
embedding of the manifold in $\mathbb{R}^N$. Our work complements a large body
of work on manifold learning, where the goal is to recover a manifold from
sampled points sampled in the manifold along with their (approximate)
distances.
