# Drawing Large Graphs by Multilevel Maxent-Stress Optimization

arXiv: https://arxiv.org/abs/1506.04383 · HTML: https://mlsanity.com/paper/1506.04383/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2015.

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

- [CoRe-GD: A Hierarchical Framework for Scalable Graph Visualization with GNNs](https://mlsanity.com/paper/2402.06706/) (2024)

## Abstract (arXiv)

Drawing large graphs appropriately is an important step for the visual analysis of data from real-world networks. Here we present a novel multilevel algorithm to compute a graph layout with respect to a recently proposed metric that combines layout stress and entropy. As opposed to previous work, we do not solve the linear systems of the maxent-stress metric with a typical numerical solver. Instead we use a simple local iterative scheme within a multilevel approach. To accelerate local optimization, we approximate long-range forces and use shared-memory parallelism. Our experiments validate the high potential of our approach, which is particularly appealing for dynamic graphs. In comparison to the previously best maxent-stress optimizer, which is sequential, our parallel implementation is on average 30 times faster already for static graphs (and still faster if executed on one thread) while producing a comparable solution quality.