Random Walk Diffusion for Efficient Large-Scale Graph Generation
<|reference_start|>Random Walk Diffusion for Efficient Large-Scale Graph Generation: 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.<|reference_end|>
@article{bernecker2024random,
    title={Random Walk Diffusion for Efficient Large-Scale Graph Generation},
    author={Tobias Bernecker, Ghalia Rehawi, Francesco Paolo Casale, Janine
  Knauer-Arloth, Annalisa Marsico},
    journal={arXiv preprint arXiv:2408.04461},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04461},
    primaryClass={cs.LG cs.SI}
}