# DRGraph: An Efficient Graph Layout Algorithm for Large-scale Graphs by Dimensionality Reduction

arXiv: https://arxiv.org/abs/2008.07799 · HTML: https://mlsanity.com/paper/2008.07799/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [Balancing between the Local and Global Structures (LGS) in Graph Embedding](https://mlsanity.com/paper/2308.16403/) (2023)
- [Node Embeddings via Neighbor Embeddings](https://mlsanity.com/paper/2503.23822/) (2025)
- [Measuring Time-Series Dataset Similarity using Wasserstein Distance](https://mlsanity.com/paper/2507.22189/) (2025)
- [Lens functions for exploring UMAP Projections with Domain Knowledge](https://mlsanity.com/paper/2405.09204/) (2024)
- [Inductive Graph Layout with Implicit Neural Fields](https://mlsanity.com/paper/2608.08876/) (2026)

## Abstract (arXiv)

Efficient layout of large-scale graphs remains a challenging problem: the force-directed and dimensionality reduction-based methods suffer from high overhead for graph distance and gradient computation. In this paper, we present a new graph layout algorithm, called DRGraph, that enhances the nonlinear dimensionality reduction process with three schemes: approximating graph distances by means of a sparse distance matrix, estimating the gradient by using the negative sampling technique, and accelerating the optimization process through a multi-level layout scheme. DRGraph achieves a linear complexity for the computation and memory consumption, and scales up to large-scale graphs with millions of nodes. Experimental results and comparisons with state-of-the-art graph layout methods demonstrate that DRGraph can generate visually comparable layouts with a faster running time and a lower memory requirement.