  We consider a point cloud $X_n := \{ x_1, \dots, x_n \}$ uniformly
distributed on the flat torus $\mathbb{T}^d : = \mathbb{R}^d / \mathbb{Z}^d $,
and construct a geometric graph on the cloud by connecting points that are
within distance $\varepsilon$ of each other. We let $\mathcal{P}(X_n)$ be the
space of probability measures on $X_n$ and endow it with a discrete Wasserstein
distance $W_n$ as introduced independently by Chow et al, Maas, and Mielke for
general finite Markov chains. We show that as long as $\varepsilon=
\varepsilon_n$ decays towards zero slower than an explicit rate depending on
the level of uniformity of $X_n$, then the space $(\mathcal{P}(X_n), W_n)$
converges in the Gromov-Hausdorff sense towards the space of probability
measures on $\mathbb{T}^d$ endowed with the Wasserstein distance. The analysis
presented in this paper is a first step in the study of stability of evolution
equations defined over random point clouds as the number of points grows to
infinity.
