# Word2VecGD: Neural Graph Drawing with Cosine-Stress Optimization

arXiv: https://arxiv.org/abs/2509.17333 · HTML: https://mlsanity.com/paper/2509.17333/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Graph Drawing via Gradient Descent, (GD)2](https://mlsanity.com/paper/2008.05584/) (2020)
- [Multicriteria Scalable Graph Drawing via Stochastic Gradient Descent, (SGD)2](https://mlsanity.com/paper/2112.01571/) (2021)
- [A Scalable Method for Readable Tree Layouts](https://mlsanity.com/paper/2305.09925/) (2023)

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

- [Inductive Graph Layout with Implicit Neural Fields](https://mlsanity.com/paper/2608.08876/) (2026)

## Abstract (arXiv)

We propose a novel graph visualization method leveraging random walk-based embeddings to replace costly graph-theoretical distance computations. Using word2vec-inspired embeddings, our approach captures both structural and semantic relationships efficiently. Instead of relying on exact shortest-path distances, we optimize layouts using cosine dissimilarities, significantly reducing computational overhead. Our framework integrates differentiable stress optimization with stochastic gradient descent (SGD), supporting multi-criteria layout objectives. Experimental results demonstrate that our method produces high-quality, semantically meaningful layouts while efficiently scaling to large graphs. Code available at: https://github.com/mlyann/graphv_nn