  Sequential lateration is a class of methods for multidimensional scaling
where a suitable subset of nodes is first embedded by some method, e.g., a
clique embedded by classical scaling, and then the remaining nodes are
recursively embedded by lateration. A graph is a lateration graph when it can
be embedded by such a procedure. We provide a stability result for a particular
variant of sequential lateration. We do so in a setting where the
dissimilarities represent noisy Euclidean distances between nodes in a
geometric lateration graph. We then deduce, as a corollary, a perturbation
bound for stress minimization. To argue that our setting applies broadly, we
show that a (large) random geometric graph is a lateration graph with high
probability under mild conditions, extending a previous result of Aspnes et al
(2006).
