# Estimating Network Dimension When the Spectrum Struggles

arXiv: https://arxiv.org/abs/2306.14266 · HTML: https://mlsanity.com/paper/2306.14266/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2023.

## Built on by (most cited first, 1 of 1)

- [Simplicity within biological complexity](https://mlsanity.com/paper/2405.09595/) (2024)

## Abstract (arXiv)

What is the dimension of a network? Here, we view it as the smallest dimension of Euclidean space into which nodes can be embedded so that pairwise distances accurately reflect the connectivity structure. We show that a recently proposed and extremely efficient algorithm for data clouds, based on computing first and second nearest neighbour distances, can be used as the basis of an approach for estimating the dimension of a network with weighted edges. We also show how the algorithm can be extended to unweighted networks when combined with spectral embedding. We illustrate the advantages of this technique over the widely-used approach of characterising dimension by visually searching for a suitable gap in the spectrum of the Laplacian.