# A Sparse Stress Model

arXiv: https://arxiv.org/abs/1608.08909 · HTML: https://mlsanity.com/paper/1608.08909/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2016.

## Built on by (most cited first, 6 of 6)

- [Balancing between the Local and Global Structures (LGS) in Graph Embedding](https://mlsanity.com/paper/2308.16403/) (2023)
- [AutoDOViz: Human-Centered Automation for Decision Optimization](https://mlsanity.com/paper/2302.09688/) (2023)
- [FORBID: Fast Overlap Removal By stochastic gradIent Descent for Graph Drawing](https://mlsanity.com/paper/2208.10334/) (2022)
- [Learning Isometric Embeddings of Road Networks using Multidimensional Scaling](https://mlsanity.com/paper/2504.17534/) (2025)
- [Tired of Over-smoothing? Stress Graph Drawing Is All You Need!](https://mlsanity.com/paper/2211.10579/) (2022)
- [Inductive Graph Layout with Implicit Neural Fields](https://mlsanity.com/paper/2608.08876/) (2026)

## Abstract (arXiv)

Force-directed layout methods constitute the most common approach to draw general graphs. Among them, stress minimization produces layouts of comparatively high quality but also imposes comparatively high computational demands. We propose a speed-up method based on the aggregation of terms in the objective function. It is akin to aggregate repulsion from far-away nodes during spring embedding but transfers the idea from the layout space into a preprocessing phase. An initial experimental study informs a method to select representatives, and subsequent more extensive experiments indicate that our method yields better approximations of minimum-stress layouts in less time than related methods.