Reconstruction of Random Geometric Graphs: Breaking the Omega(r) distortion barrier

TL;DR

This paper presents a new algorithm for reconstructing vertex positions in random geometric graphs with improved accuracy, surpassing the previous limit of error proportional to the connection radius, and extends the approach to other geometric domains.

Contribution

The authors introduce an algorithm that achieves near-optimal reconstruction error bounds for random geometric graphs, surpassing the Omega(r) barrier, and extend the methodology to spherical and hypercube domains.

Findings

Reconstruction error is reduced to O(n^β) for α > 0, improving over previous methods.

The method extends to spherical surfaces and hypercubes in fixed dimensions.

Reconstruction remains feasible under random edge deletions.

Abstract

Embedding graphs in a geographical or latent space, i.e.\ inferring locations for vertices in Euclidean space or on a smooth manifold or submanifold, is a common task in network analysis, statistical inference, and graph visualization. We consider the classic model of random geometric graphs where $n$ points are scattered uniformly in a square of area $n$, and two points have an edge between them if and only if their Euclidean distance is less than $r$. The reconstruction problem then consists of inferring the vertex positions, up to the symmetries of the square, given only the adjacency matrix of the resulting graph. We give an algorithm that, if $r=n^\alpha$ for any $\alpha > 0$, with high probability reconstructs the vertex positions with a maximum error of $O(n^\beta)$ where $\beta=1/2-(4/3)\alpha$, until $\alpha \ge 3/8$ where $\beta=0$ and the error becomes $O(\sqrt{\log n})$. This improves over earlier results, which were unable to reconstruct with error less than $r$. Our method estimates Euclidean distances using a hybrid of graph distances and short-range estimates based on the number of common neighbors. We extend our results to the surface of the sphere in $\R^3$ and to hypercubes in any constant fixed dimension. Additionally we examine the extent to which reconstruction is still possible when the original adjacency lists have had a subset of the edges independently deleted at random.