用词向量思想优化图布局，速度快且保留语义信息。
Word2VecGD: Neural Graph Drawing with Cosine-Stress Optimization
- 用随机游走+Word2Vec生成节点嵌入，替代耗时的最短路径计算。
- 通过余弦差异优化布局，大图上仍能保持高质量视觉效果。
- 支持多目标布局，适合需要快速生成语义图的场景。
我们提出一种新型图可视化方法，利用基于随机游走的嵌入代替昂贵的图论距离计算。采用受Word2Vec启发的嵌入方式，高效捕捉节点间的结构与语义关系。不依赖精确的最短路径距离，而是通过余弦差异优化布局，显著降低计算开销。框架结合可微应力优化与随机梯度下降（SGD），支持多准则布局目标。实验表明，该方法在大规模图上仍能生成高质量、具有语义意义的布局。代码已开源：https://github.com/mlyann/graphv_nn
原文摘要 · Abstract (English)
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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