Random Walk Diffusion for Efficient Large-Scale Graph Generation
Abstract
Graph generation addresses the problem of generating new graphs that have a data distribution similar to real-world graphs. While previous diffusion-based graph generation methods have shown promising results, they often struggle to scale to large graphs. In this work, we propose ARROW-Diff (AutoRegressive RandOm Walk Diffusion), a novel random walk-based diffusion approach for efficient large-scale graph generation. Our method encompasses two components in an iterative process of random walk sampling and graph pruning. We demonstrate that ARROW-Diff can scale to large graphs efficiently, surpassing other baseline methods in terms of both generation time and multiple graph statistics, reflecting the high quality of the generated graphs.
Cite
@article{arxiv.2408.04461,
title = {Random Walk Diffusion for Efficient Large-Scale Graph Generation},
author = {Tobias Bernecker and Ghalia Rehawi and Francesco Paolo Casale and Janine Knauer-Arloth and Annalisa Marsico},
journal= {arXiv preprint arXiv:2408.04461},
year = {2025}
}
Comments
Published as a paper at Transactions on Machine Learning Research (TMLR) in March 2025