Word2VecGD: Neural Graph Drawing with Cosine-Stress Optimization
This addresses the problem of computational overhead in graph drawing for researchers and practitioners, though it is incremental as it builds on existing embedding methods.
The paper tackled graph visualization by replacing costly distance computations with random walk-based embeddings, resulting in high-quality layouts that scale efficiently to large graphs.
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