Learning random points from geometric graphs or orderings

TL;DR

This paper demonstrates that given either a geometric graph or distance orderings of points, one can efficiently reconstruct the original point configuration with high accuracy, under certain conditions.

Contribution

It introduces algorithms for reconstructing point embeddings from geometric graphs and orderings, achieving high accuracy in polynomial time.

Findings

Reconstruction from geometric graphs is possible with displacement about the threshold distance r.

Reconstruction from orderings achieves displacement error of O(√log n).

Algorithms work with high probability under specified conditions.

Abstract

Suppose that there is a family of $n$ random points $X_v$ for $v \in V$, independently and uniformly distributed in the square $\left[-\sqrt{n}/2,\sqrt{n}/2\right]^2$ of area $n$. We do not see these points, but learn about them in one of the following two ways. Suppose first that we are given the corresponding random geometric graph $G$, where distinct vertices $u$ and $v$ are adjacent when the Euclidean distance $d_E(X_u,X_v)$ is at most $r$. If the threshold distance $r$ satisfies $n^{3/14} \ll r \ll n^{1/2}$, then the following holds with high probability. Given the graph $G$ (without any geometric information), in polynomial time we can approximately reconstruct the hidden embedding, in the sense that, `up to symmetries', for each vertex $v$ we find a point within distance about $r$ of $X_v$; that is, we find an embedding with `displacement' at most about $r$. Now suppose that, instead of being given the graph $G$, we are given, for each vertex $v$, the ordering of the other vertices by increasing Euclidean distance from $v$. Then, with high probability, in polynomial time we can find an embedding with the much smaller displacement error $O(\sqrt{\log n})$.