Random Walk Diffusion for Efficient Large-Scale Graph Generation

• URL: http://arxiv.org/abs/2408.04461v1
• Date: Thu, 8 Aug 2024 13:42:18 GMT
• Title: Random Walk Diffusion for Efficient Large-Scale Graph Generation
• Authors: Tobias Bernecker, Ghalia Rehawi, Francesco Paolo Casale, Janine Knauer-Arloth, Annalisa Marsico,
• Abstract summary: We propose ARROW-Diff (AutoRegressive RandOm Walk Diffusion), a novel random walk-based diffusion approach for efficient large-scale graph generation. 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.
• Score: 0.43108040967674194
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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