# A Distributed Multilevel Force-directed Algorithm

arXiv: https://arxiv.org/abs/1608.08522 · HTML: https://mlsanity.com/paper/1608.08522/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2016.

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

- [Graph Force Learning](https://mlsanity.com/paper/2103.04344/) (2021)
- [Physics-Informed Graph Learning](https://mlsanity.com/paper/2202.10679/) (2022)

## Abstract (arXiv)

The wide availability of powerful and inexpensive cloud computing services naturally motivates the study of distributed graph layout algorithms, able to scale to very large graphs. Nowadays, to process Big Data, companies are increasingly relying on PaaS infrastructures rather than buying and maintaining complex and expensive hardware. So far, only a few examples of basic force-directed algorithms that work in a distributed environment have been described. Instead, the design of a distributed multilevel force-directed algorithm is a much more challenging task, not yet addressed. We present the first multilevel force-directed algorithm based on a distributed vertex-centric paradigm, and its implementation on Giraph, a popular platform for distributed graph algorithms. Experiments show the effectiveness and the scalability of the approach. Using an inexpensive cloud computing service of Amazon, we draw graphs with ten million edges in about 60 minutes.