# FORBID: Fast Overlap Removal By stochastic gradIent Descent for Graph Drawing

arXiv: https://arxiv.org/abs/2208.10334 · HTML: https://mlsanity.com/paper/2208.10334/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 19 Aug 2022.

## Builds on (most cited first, 5 of 5)

- [DeepGD: A Deep Learning Framework for Graph Drawing Using GNN](https://mlsanity.com/paper/2106.15347/) (2021)
- [A Sparse Stress Model](https://mlsanity.com/paper/1608.08909/) (2016)
- [Deep Neural Network for DrawiNg Networks, (DNN)^2](https://mlsanity.com/paper/2108.03632/) (2021)
- [Node Overlap Removal by Growing a Tree](https://mlsanity.com/paper/1608.02653/) (2016)
- [Node Overlap Removal Algorithms: A Comparative Study](https://mlsanity.com/paper/1908.07363/) (2019)

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

- [Bridging Graph Drawing and Dimensionality Reduction with Stochastic Stress Optimization](https://mlsanity.com/paper/2605.00641/) (2026)

## Abstract (arXiv)

While many graph drawing algorithms consider nodes as points, graph visualization tools often represent them as shapes. These shapes support the display of information such as labels or encode various data with size or color. However, they can create overlaps between nodes which hinder the exploration process by hiding parts of the information. It is therefore of utmost importance to remove these overlaps to improve graph visualization readability. If not handled by the layout process, Overlap Removal (OR) algorithms have been proposed as layout post-processing. As graph layouts usually convey information about their topology, it is important that OR algorithms preserve them as much as possible. We propose a novel algorithm that models OR as a joint stress and scaling optimization problem, and leverages efficient stochastic gradient descent. This approach is compared with state-of-the-art algorithms, and several quality metrics demonstrate its efficiency to quickly remove overlaps while retaining the initial layout structures.