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
@article{arxiv.2509.17333,
title = {Word2VecGD: Neural Graph Drawing with Cosine-Stress Optimization},
author = {Minglai Yang and Reyan Ahmed},
journal= {arXiv preprint arXiv:2509.17333},
year = {2025}
}