# Interpreting Graph Drawing with Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2011.00748 · HTML: https://mlsanity.com/paper/2011.00748/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

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

- [Using Reinforcement Learning to Optimize the Global and Local Crossing Number](https://mlsanity.com/paper/2509.06108/) (2025)

## Abstract (arXiv)

Applying machine learning techniques to graph drawing has become an emergent area of research in visualization. In this paper, we interpret graph drawing as a multi-agent reinforcement learning (MARL) problem. We first demonstrate that a large number of classic graph drawing algorithms, including force-directed layouts and stress majorization, can be interpreted within the framework of MARL. Using this interpretation, a node in the graph is assigned to an agent with a reward function. Via multi-agent reward maximization, we obtain an aesthetically pleasing graph layout that is comparable to the outputs of classic algorithms. The main strength of a MARL framework for graph drawing is that it not only unifies a number of classic drawing algorithms in a general formulation but also supports the creation of novel graph drawing algorithms by introducing a diverse set of reward functions.