# Spherical Graph Drawing by Multi-dimensional Scaling

arXiv: https://arxiv.org/abs/2209.00191 · HTML: https://mlsanity.com/paper/2209.00191/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2022.

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

- [Linearized Wasserstein dimensionality reduction with approximation guarantees](https://mlsanity.com/paper/2302.07373/) (2023)
- [Balancing between the Local and Global Structures (LGS) in Graph Embedding](https://mlsanity.com/paper/2308.16403/) (2023)

## Abstract (arXiv)

We describe an efficient and scalable spherical graph embedding method. The method uses a generalization of the Euclidean stress function for Multi-Dimensional Scaling adapted to spherical space, where geodesic pairwise distances are employed instead of Euclidean distances. The resulting spherical stress function is optimized by means of stochastic gradient descent. Quantitative and qualitative evaluations demonstrate the scalability and effectiveness of the proposed method. We also show that some graph families can be embedded with lower distortion on the sphere, than in Euclidean and hyperbolic spaces.