{"ID":2867472,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.17333","arxiv_id":"2509.17333","title":"Word2VecGD: Neural Graph Drawing with Cosine-Stress Optimization","abstract":"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","short_abstract":"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 optim...","url_abs":"https://arxiv.org/abs/2509.17333","url_pdf":"https://arxiv.org/pdf/2509.17333v1","authors":"[\"Minglai Yang\",\"Reyan Ahmed\"]","published":"2025-09-22T03:09:55Z","proceeding":"cs.CG","tasks":"[\"cs.CG\",\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":609466,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2867472,"paper_url":"https://arxiv.org/abs/2509.17333","paper_title":"Word2VecGD: Neural Graph Drawing with Cosine-Stress Optimization","repo_url":"https://github.com/mlyann/graphv_nn","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}