# Distance Geometry and Data Science

arXiv: https://arxiv.org/abs/1909.08544 · HTML: https://mlsanity.com/paper/1909.08544/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Sample-Efficient Geometry Reconstruction from Euclidean Distances using Non-Convex Optimization](https://mlsanity.com/paper/2410.16982/) (2024)
- [Distance-Geometric Graph Attention Network (DG-GAT) for 3D Molecular Geometry](https://mlsanity.com/paper/2207.08023/) (2022)
- [Euclidean Distance Matrix Completion via Asymmetric Projected Gradient Descent](https://mlsanity.com/paper/2504.19530/) (2025)

## Abstract (arXiv)

Data are often represented as graphs. Many common tasks in data science are based on distances between entities. While some data science methodologies natively take graphs as their input, there are many more that take their input in vectorial form. In this survey we discuss the fundamental problem of mapping graphs to vectors, and its relation with mathematical programming. We discuss applications, solution methods, dimensional reduction techniques and some of their limits. We then present an application of some of these ideas to neural networks, showing that distance geometry techniques can give competitive performance with respect to more traditional graph-to-vector mappings.