Word2VecGD: Neural Graph Drawing with Cosine-Stress Optimization

2025-09-21

AI Summary

Computing graph-theoretic distances (e.g., shortest paths) for large-scale graph visualization is computationally expensive and fails to jointly preserve structural and semantic relationships. Method: We propose an end-to-end graph layout learning framework that integrates random-walk-based node embedding—inspired by word2vec—with differentiable cosine-stress optimization. Specifically, truncated random walks generate node sequences to learn low-dimensional embeddings that implicitly encode both topological and semantic proximity; a differentiable cosine-based stress function replaces traditional non-differentiable shortest-path objectives, enabling efficient optimization via stochastic gradient descent. Contribution/Results: Our method achieves high-quality, semantics-aware layouts on graphs with up to one million nodes, significantly reducing computational complexity while maintaining strong scalability. The implementation is open-sourced.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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

Problem

• Replacing costly graph distance computations with efficient embeddings
• Optimizing layouts using cosine dissimilarities instead of shortest paths
• Scaling graph visualization to large networks while maintaining quality

Innovation

• Uses word2vec-inspired random walk embeddings
• Optimizes layouts with cosine dissimilarities
• Integrates differentiable stress optimization with SGD