Word2VecGD: Neural Graph Drawing with Cosine-Stress Optimization

• URL: http://arxiv.org/abs/2509.17333v1
• Date: Mon, 22 Sep 2025 03:09:55 GMT
• Title: Word2VecGD: Neural Graph Drawing with Cosine-Stress Optimization
• Authors: Minglai Yang, Reyan Ahmed,
• Abstract summary: Instead of relying on exact shortest-path distances, we optimize layouts using cosine dissimilarities.<n>Our framework integrates differentiable stress optimization with gradient descent.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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