# BatchLayout: A Batch-Parallel Force-Directed Graph Layout Algorithm in Shared Memory

arXiv: https://arxiv.org/abs/2002.08233 · HTML: https://mlsanity.com/paper/2002.08233/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods](https://mlsanity.com/paper/2112.10372/) (2021)
- [SparseTransX: Efficient Training of Translation-Based Knowledge Graph Embeddings Using Sparse Matrix Operations](https://mlsanity.com/paper/2502.16949/) (2025)

## Abstract (arXiv)

Force-directed algorithms are widely used to generate aesthetically pleasing layouts of graphs or networks arisen in many scientific disciplines. To visualize large-scale graphs, several parallel algorithms have been discussed in the literature. However, existing parallel algorithms do not utilize memory hierarchy efficiently and often offer limited parallelism. This paper addresses these limitations with BatchLayout, an algorithm that groups vertices into minibatches and processes them in parallel. BatchLayout also employs cache blocking techniques to utilize memory hierarchy efficiently. More parallelism and improved memory accesses coupled with force approximating techniques, better initialization, and optimized learning rate make BatchLayout significantly faster than other state-of-the-art algorithms such as ForceAtlas2 and OpenOrd. The visualization quality of layouts from BatchLayout is comparable or better than similar visualization tools. All of our source code, links to datasets, results and log files are available at https://github.com/khaled-rahman/BatchLayout.